Posted: November 23rd, 2022

0000plagiarisim

Trident University International

Student Name

Don't use plagiarized sources. Get Your Custom Essay on
0000plagiarisim
Just from $13/Page
Order Essay

Supply Chain Management Module 3 SLP

LOG301: Introduction to Supply Chain Management

Professor’s Name

Date of Submission

Supply Chain Management Module 3 SLP

2-3 sentence introduction. Let us continue with our beer theme.

Start a new game

and play one year as a Wholesaler and a second game as Retailer.

Game experience: debrief

Describe the wholesaler and retailer’s results based on debrief content that you have placed in the appendices of the paper. (1/2 page)

Game experience: debrief analysis

Through research, analyze the results and apply to real-world applications (research required). (1/2 page)

Understanding chain relationships

Research best practices for relationships within the chain. (research required) Connect with game results. Assess how the retailer and wholesaler are affected in the chain. (research required) (1 page)

The consumer

Research consumer behavior and economic implications of game scenarios. (research required) (1/2 page) Effective academic and research writing requires a 3rd person voice. This SLP will be written in 3rd person. Do not use any quotations. Refer to yourself in third-person as “participant” or you can write about what the “player” will do, rather than saying “I” and “we.” Since you are engaging in research, be sure to cite and reference the sources in APA format. NOTE: failure to use research with accompanying citations to support content will result in reduced scoring “Level 2-Developing” for Assignment-Driven Criteria, Critical Thinking and Assignment Organization and Quality of

References

on the grading rubric.

Conclusion

This is your 2-3 sentence conclusion. Remember this is the last thing your reader will hear.
Your submission will include:

· Trident University International’s cover page

· The body of the paper will be 2 ½ -pages (2-3 sentence introduction, 2 ½ page body, and 2-3 sentence conclusion)

· Appendices (pages vary based on size of screenshots)

The alphabetized reference list page in APA format

Appendices

Screenshot and paste the Debriefs for each of the two games you played

·

Wholesaler Role Game 1: Global results

·

Wholesaler Role Game 1: The four charts

·

Retailer Role Game 2: Global results

·

Retailer Role Game 2: The four charts

References

This listing should be in alphabetical order. Below are a few examples of reference list entries. The following list needs to be removed before you submit the paper.

Journal in online library (be sure that you give the specific library database for journal articles that you have retrieved from the library, e.g., Proquest, EBSCO – Academic Search Complete, EBSCO – Business Source Complete, IBISWorld, etc.):

Last name, Initials. (yyyy of journal volume). Title of article. Title of Journal, volume

number,(issue number), pages. Retrieved from [insert name of library

database]

Example:

Borgerson, J. L., Schroeder, J. E., Escudero Magnusson, M., & Magnusson, F. (2009).

Corporate communication, ethics, and operational identity: A case study of Benetton. Business Ethics: A European Review, 18(3), 209-223. Retrieved from Proquest.

Book in online library:

Last name, Initials. (yyyy published). Book title. Retrieved from [insert name of library

database]

Example:

Johnson, R. A. (2009). Helping really fat dogs. Retrieved from EBSCO eBook Collection.

Newspaper in online library:

Author last name, first initial. (YYYY, MM DD). Name of article. Title of Newspaper,

pages. Retrieved from [name of library database].

Example:

Dee, J. (2007, December 23). A toy maker’s conscience. New York Times Magazine, 34-39.

Retrieved from EBSCO – Academic Source Complete.

Websites

APA end reference for a website – with author:

Author. (Year [use n.d. if not given]). Article or page title.

Larger Publication Title. Retrieved from

https://urladdress

Example:

Shiva, V. (2006, February 12). Bioethics: A third world issue. Nativeweb. Retrieved

from

https://www.nativeweb.org/pages/legal/shiva.html

APA end reference for a website – with no author:

Title of article. (Year [use n.d. if not given]). Website Title. Retrieved from

https://www.website-name/ABCDEFG-12345

Example:

Media giants. (2014). Frontline: The Merchants of Cool. Retrieved from

https://www.pbs.org/wgbh/pages/frontline/shows/cool/giants/

Running head: SUPPLY CHAIN MANAGEMENT MODULE 3 SLP 1

SUPPLY CHAIN MANAGEMENT MODULE 3 SLP 2

Appendix A

Wholesaler Role Game 1: Global results

Appendix B

Wholesaler Role Game 1: The four charts

Appendix C

Retailer Role Game 2: Global results

Appendix D

Retailer Role Game 2: The four charts

Module 3 SLP Assignment

Play the Beer game. Link to Start a new game.

https://www.masystem.se/MA-system-Consulting/Play-The-Beer-Game

.

The LOG301 Template will be added separately.

Module 3 Case Assignment.

Attached is the required reading for this assignment. The two articles will be added separately. Please read the two articles and complete the case.

Article One and Article Two will be added separately.

Contents lists available at ScienceDirect

T

ourism Management

journal homepage: www.elsevier.com/locate/tourman

Using big data from Customer Relationship Management information
systems to determine the client profile in the hotel sector

Pilar Talón-Ballesteroa, Lydia González-Serranoa,∗, Cristina Soguero-Ruizc,
Sergio Muñoz-Romerob,c, José Luis Rojo-Álvarezb,c

a Department of Business Economics, Rey Juan Carlos University, Camino del Molino s/n, 28943 Fuenlabrada, Madrid, Spain
b Center for Computational Simulation, Universidad Politécnica de Madrid, Boadilla, 28223 Madrid, Spain
c Department of Signal Theory and Communications and Telematic Systems and Computation, Rey Juan Carlos University, Camino del Molino s/n, 28943 Fuenlabrada,
Madrid, Spain

A R T I C L E I N F O

Keywords:
Big data
Hospitality industry
Customer relationship management
Client profile
Bootstrap resampling
Hotel chains

A B S T R A C T

Client knowledge remains a key strategic point in hospitality management. However, the role that can be played
by large amounts of available information in the Customer Relationship Management (CRM) systems, when
addressed by using emerging Big Data techniques for efficient client profiling, is still in its early stages. In this
work, we addressed the client profile of the data in a CRM system of an international hotel chain, by using Big
Data technology and Bootstrap resampling techniques for Proportion Tests. Strong consistency was found on the
most representative feature of repeaters being traveling without children. Profiles were more similar for British and
German clients, and their main differences with Spanish clients were in the stay duration and in age. For a
vacation chain, these results suggest further analysis on the target orientation towards new market segments. Big
Data technologies can be extremely useful for analyzing indoor data available in CRM information systems from
hospitality industry.

1. Introduction

Customer knowledge is vital for the hospitality industry, and it plays
a crucial role in improving the offer with better quality services (i.e.,
more adapted and customized), the relationship with customers, and
the approach of marketing strategies (Adomavicius & Tuzhilin, 2001;
Min, Min & Emam, 2002). All of them result in better customer sa-
tisfaction that increases the loyalty and ensures repeating customers, as
well as higher profitability (Tseng & Wu, 2014). Over the last several
years, this information has been mainly managed in many hotels by
proactively gathering and recording customer preferences into the so-
called Customer Relationship Management (CRM) systems
(Sarmaniotis, Assimakopoulos, & Papaioannou, 2013). CRMs have be-
come a key strategy for improving customer satisfaction and retention,
especially in hotels (Padilla-Meléndez & Garrido-Moreno, 2013), and
they are remarkably beneficial to those organizations by generating
large amounts of valuable information about their customers (Chadha,
2015; Kotler, 2002; Nguyen, Sherif, & Newby, 2007).

Nevertheless, it has been recently pointed out (Dursun & Caber,
2016) that even advanced analysis techniques, such as data mining, are

not yet being adequately used in the hotel industry for the purpose of
effectively profiling the customers by using the comprehensive data
that are routinely collected with hotel CRM systems. A large amount of
information is available nowadays in hotel companies, either internal
and structured (from the Property Management and the CRM systems),
or external and unstructured (such as opinion platforms, social net-
works, or geolocalization, among many others). This brings the need to
consider powerful tools available from Big Data technologies, which
have already been successfully used in other fields such as bioinfor-
matics, healthcare, or finance (George, Haas, & Pentland, 2014), to
name just a few.

Big Data technologies are providing unprecedented opportunities
for statistical inference on massive analysis, but they also bring new
challenges to be addressed, especially when compared to the analysis of
carefully collected smaller data sets. In Sivarajah, Kamal, Irani, and
Weerakkody (2017), a systematic and illustrative review is presented
on the state-of-art analysis of the literature on Big Data techniques and
Big Data Analytics, which highlights the key challenges in terms of
different data types, data processing, and data management. As pointed
therein, descriptive statistics are the simplest form of Big Data analytic

https://doi.org/10.1016/j.tourman.2018.03.017
Received 11 August 2017; Received in revised form 12 March 2018; Accepted 20 March 2018

∗ Corresponding author.
E-mail addresses: pilar.talon@urjc.es (P. Talón-Ballestero), lydia.gonzalez@urjc.es (L. González-Serrano), cristina.soguero@urjc.es (C. Soguero-Ruiz),

sergio.munoz@urjc.es (S. Muñoz-Romero), joseluis.rojo@urjc.es (J.L. Rojo-Álvarez).

Tourism Management 68 (2018) 187–

197

Available online 28 March 2018
0261-5177/ © 2018 Elsevier Ltd. All rights reserved.

T

http://www.sciencedirect.com/science/journal/02615177

https://www.elsevier.com/locate/tourman

https://doi.org/10.1016/j.tourman.2018.03.017

https://doi.org/10.1016/j.tourman.2018.03.017

mailto:pilar.talon@urjc.es

mailto:lydia.gonzalez@urjc.es

mailto:cristina.soguero@urjc.es

mailto:sergio.munoz@urjc.es

mailto:joseluis.rojo@urjc.es

https://doi.org/10.1016/j.tourman.2018.03.017

http://crossmark.crossref.org/dialog/?doi=10.1016/j.tourman.2018.03.017&domain=pdf

methods, and they involve the summarization and description of
knowledge and patterns by using simple statistical tests, such as mean,
median, mode, variance, or proportions. When scrutinizing the useful-
ness of Big Data technologies in a new application field, it is necessary
to establish well the behavior and scope of basic statistics, before going
into more sophisticated analytics such as data mining or advanced
machine learning.

In the present work, our main practical objective was to determine
the client profile in an international hotel chain by exploiting the
overall information in its CRM system. For this purpose, we identified
the relevant variable groups, and we analyzed their practical meaning
by using Big Data analytics on proportion tests from ratios between
repeaters and first-timers. The use of robust and reliable proportion
tests in this scenario has been tackled by using Bootstrap resampling
techniques, which provides us with clear cut-off tests for decision
making even in massive analysis conditions. It was possible for us to
obtain two types of implications, namely, those related with the ap-
plication of Big Data techniques to CRM exploitation, and those related
with the results derived from the specific application to this hotel chain.

The scheme of the paper is as follows. In the next section, we in-
troduce the relevance of CRM systems and their applications in the
hotel sector, the basics on Big Data techniques and their scope in cur-
rent analytics for hotel clients, and some relevant studies dealing with
client profiling in terms of their repeating behavior. Then, we present
the theoretical foundations of the methods to be used in our client
profiling study, consisting of proportion tests and on bootstrap resam-
pling. We then present the results on a real database from a large-scale
hotel chain. Discussion is established on our and others’ results, and
finally, concise conclusions are drawn.

2. Literature review

Our main objective in the present work was to determine the re-
peater client profile by exploiting CRM systems in hotel chains and
using Big Data technologies. For this purpose, we start by presenting a
review of the state of art focused on the three main topics developed
here, and the CRM system concept is first scrutinized. Recent applica-
tions on Big Data technologies are then summarized, both of them in
the hotel industry, and finally, we present recent studies analyzing the
repeaters versus first-timers profiles.

2.1. CRM in the hospitality industry

In the last several years, CRM has grown in relevance both in the
operational and in the strategic points of view. Two of the major rea-
sons for this are the increasing market competitiveness and the lower
cost for client retention than for new client recruiting (Petrick, 2004;
Yoo & Bai, 2013). Hence, CRM has become a key strategy for perso-
nalizing the client experience and for increasing their satisfaction.

The present work deals with CRM systems. A CRM system is a “firm
tool that is technology-based for developing and leveraging consumer
knowledge to nurture, maintain, and strengthen profitable relationships
with consumers” (Elfving & Lemoine, 2012). According to Buttle
(2004), a CRM system is a crucial part of a global CRM strategy. Soltani
and Navimipour (2016) stated that CRM systems provide the infra-
structure that facilitates the construction of long-term relationships
with customers. Some examples of the functionality of CRM systems are
sales force automation, data warehousing, data mining, decision sup-
port, and reporting tools (Hendricks, Singhal & Startman, 2007; Katz,
2002; Soltani & Navimipour, 2016).

For the hospitality sector, several studies consider CRM as one of the
best strategies for improving a company’s results and for ensuring long-
term survival (Abu Kasim & Minai, 2009; Keramati, Mehrabi, & Mojir,
2010; Kim & Choi, 2010; Sigala, 2005; Wu & Li, 2011). Accordingly,
CRM systems are nowadays a fundamental tool in the hotel sector,
especially when properly implemented, due to the large amount of data

that hotels integrate from their clients. These data could be turned into
useful knowledge (Chadha, 2015; Dev & Olsen, 2000; Kotler, 2002; Lin
& Su, 2003; Nasution & Mavondo, 2008; Nguyen et al., 2007), and the
implementation of CRM systems allows us to identify the host beha-
vioral patterns and to retain them in the long term (Chadha, 2015;
Papastathopoulou, Avlonitis, & Panagopoulos, 2007; Verdugo, Oviedo-
Garcia, & Roldan, 2009).

It is evident that customer loyalty and profitability are correlated
(Payne & Frow, 2005). Therefore, one of the main assumptions of CRM
systems is that satisfying and creating long-term relationships with
profitable customers enhances the business success of the company (Wu
& Lu, 2012). However, the role that large amounts of information
currently available in CRM systems can play in efficient client profiling
has not been studied enough yet, even for simple and well known sta-
tistical descriptions. In addition, there is evidence that advanced ana-
lysis techniques are not yet being properly used in the hotel industry to
effectively profile customers from comprehensive data collected via
hotel CRM systems (Dursun & Caber, 2016). Hotels are not fully ex-
ploiting the potential of CRM systems, but there is strong interest and
ongoing work towards their successful implementation (Padilla-
Melendez & Garrido-Moreno, 2013). This way, both the CRM-effort
efficiency and a company’s competitiveness could be dramatically in-
creased.

2.2. Big data in the hospitality industry

Big Data is drastically changing the hotel sector management and
the client-to-business relationship, by making the decision-making
process from large amounts of data easier (Fox & Do, 2013). Nowadays,
the technological bases of both the tourism organizations and the ho-
teliers make relevant that marketers and managers improve their access
to data intelligence to make the best use of it (Peter, 2014). These
professionals have invested heavily in recent years in organizing strong
scientific teams and including statisticians and database experts who
are well equipped to build and analyze the contents of their Data
Warehouses (Ramos et al., 2017). Though human analysis is often re-
quired, today Big Data can enhance the decision making and increase
the organizational output from five possible approaches, namely, de-
scriptive analytics, inquisitive analytics, predictive analytics, pre-
scriptive analytics, and preemptive analytics. Most Big Data analytics
are descriptive and exploratory in nature, but even simple descriptive
statistics allow businesses to discover simple and clear patterns that
become extremely useful for decisions.

The hospitality industry has become an information intense sector,
where large data volumes have been stored with practical applications
which are not so widespread. With the arrival of Big Data, it is possible
to manage such data to achieve the objectives and to transform the
information into knowledge (Xiang, Schwartz, Gerdes & Uysal, 2015).
Data are stored in very different formats, and their analysis becomes a
complex task due to their heterogeneity, going from structured data in
conventional databases (from Property Management Systems and CRM
systems) to semi-structured and unstructured data. Furthermore, the
available information systems often can include meta-search generated
data, e.g., Tripadvisor, Kayak, Trivago, or social networks such as Fa-
cebook, Twitter, or LinkedIn (Ramos et al., 2017; Santana-Cerdeña,
Ramos, & Bobur, 2014).

The hotel industry is starting to use Big Data technologies mainly in
product sales, social media and online behavior of customers, as well as
offline data retrieval and analysis (Zhang, Shu & Wang, 2015). Some
examples are tourist’s location (Hjorth, 2012; Silva & Mateus, 2003; Vu,
Li, Law, & Ye, 2015), blogs (Litvin, Goldsmith, & Pan, 2008; Tseng, Wu,
Morrison, Zhang, & Chen, 2015), photography (Balomenou & Garrod,
2014), internet behavior (Rong, Vu, Law, & Li, 2012), search engines
(Pan & Li, 2011), and Online Travel Agencies (Ramos et al., 2017), to
cite just a few. There is a growing interest in the hospitality field to
exploit user-generated data and gain insight into research problems that

P. Talón-Ballestero et al. Tourism Management 68 (2018) 187–197

188

have not yet been well understood using conventional methods (Yang,
Pan, & Song, 2014; Ye, Law, & Gu, 2009). The most used Big Data
method in this setting is Text Analytics for information retrieval, and it
usually involves machine learning, statistical analysis, and computa-
tional linguistics (Özköse, Arı, & Gencer, 2015). The main functional-
ities of Big Data and CRM systems in hotel management focus on
Revenue Management and on Marketing, and their use includes fore-
casting, pricing, and bench marking (Haynes, 2016; Noone, 2016; Pan
& Yang, 2016; Ramos et al., 2017; Song & Liu, 2017).

However, the growing interest in Big Data for dealing with external
and unstructured data from clients has brought attention to the fact that
large hotel chains can also access a huge amount of internal and
structured data through their CRM systems. These systems may not
often be deeply exploited for client knowledge purposes, even though
simple statistical analysis in Big Data can yield significant and powerful
pattern descriptions. Whereas some other studies have paid attention to
Big Data approaches with advanced analytics and to its usefulness on
internal data generated by hotel clients (Lee, Hwang, Jo, & Kim, 2016),
the present study aims to gain insight specifically on the client profiling
and restricting ourselves to simple Big Data analytics tools, such as
proportion tests.

2.3. Client profiling in first-timers vs repeaters

Researchers have pointed out the relevance of understanding the
differences between first-timers and repeaters in the hospitality and
tourism industry (Anwar & Sohail, 2004; Morais & Lin, 2010;
Oppermann, 1997; Petrick, 2005). Marketing researchers suggested
that understanding the differences between first-timers and repeaters
can provide us with an excellent basis for market segmentation
(Formica & Uysal, 1998; Ryu & Han, 2011). Several studies have
identified both tourist profiles, mostly focused on touristic destinations
(Fallon & Schofield, 2003; Kim & Prideaux, 2005; Lau & Mckercher,
2004; Li, Cheng, Kim, Petrick, 2008; Morais & Lin, 2010; Oppermann,
1997; Tasci, 2016; Vu et al., 2015; Wang, 2004), festival and cultural
events (Anwar & Sohail, 2004; Formica & Uysal, 1998), restaurants
(Ryu & Han, 2011), cruises (Petrick, 2004), or whitewater rafting
(Fluker & Turner, 2000). These differences are fundamental to devel-
oping effective business and marketing strategies, as well as to under-
stand client motivation and to build theoretical knowledge on decision-
making (Lau & Mckercher, 2004; Oppermann, 1997; Petrick, 2004).
However, few academic references can be found in the hotel sector
(Kim, Knutson, & Vogt, 2014).

It has been suggested (Chakravarti & Day, 1991) that different
consumer profiles should be determined and identified in terms of

different features. Some authors have pointed out the relevance of be-
havior variables, such as past buying patterns (Bayer, 2010; Kim, Jung,
Suh, & Hwang, 2006; Wind & Lerner, 1979), and even more emphasis
has been made on the relationship between the repetition habit of the
client and the loyalty (Yoo & Bai, 2013). Moreover, repeaters can be-
come efficient communication channels for relatives, friends, collea-
gues, and other potential consumers (Petrick, 2004).

On the other hand, Tasci (2016) shows that loyal consumers are
different from others in terms of socio-demographic, psychographic,
and behavioral features. Hence, demographic factors (such as educa-
tion, gender, and age), as well as travel behavior variables (including
purpose of travel), have been found to strongly influence consumer
loyalty (Homburg & Giering, 2000; Skogland & Siguaw, 2004), and they
pose notable differences between first-timers and repeaters (Lau &
Mckercher, 2004; Li et al., 2008; McKercher, 2004; Oppermann, 1997).
When socio-demographics were analyzed, significant differences were
found based on age, spending patterns, length of stay, and nationality
(Gitelson & Crompton, 1984; Li et al., 2008; McKercher, 2004).

Our study focuses on the profile of repeaters in an international
chain. McKercher (2004) and Lau & Mckercher (2004) classified tra-
velers to holiday destinations as either first-timers or repeaters. Many
holiday destinations rely heavily on repeated visitations (Anwar &
Sohail, 2004; Fallon & Schofield, 2003), hence, the results of this dif-
ferentiation can be very relevant for this sector.

3. Statistical and data analytics methods

3.1. Big database

The database used in this study was assembled from a CRM system
routinely used by a widely known international hotel company with
international scope. The Information Technology unit of the company
supervised the access to the information, and a business object was
created upon the CRM system universe allowing us to scrutinize the
stored features (variables) and to pre-filter the possibly useful ones. A
Web Intelligence document was specifically created in the system to
support this preliminary analysis.

The information of 4,935,806 different clients (those who stayed
overnight at the hotels in the chain) was recorded in the CRM system
during years 2013 and 2014. Table 1 describes the variables used in this
work. A total of 18 variables as well as their corresponding categories
were analyzed, including among them demographic variables, beha-
vioral variables, and transactional data. In addition, the information
generated by the hotel chain was also considered as relevant for the
present study: guests grouped into repeaters (those clients who stayed

Table 1
Variable description.

Group of variables Variables Categories Type Examples

Demographic Age 1 Numerical
Gender 3 Categorical Female, male, unknown
Civil status 3 Categorical Single, with partner, unknown
Country of residence > 30 Categorical Spain, Germany
State > 150 Categorical Berlin, Madrid

Behavioral Preferred destination 10 Categorical
Motivation > 150 Categorical Leisure, business
Family holidays 3 Categorical Yes, no, unknown
Traveling with a partner 3 Categorical Yes, no, unknown
Traveling with children 3 Categorical Yes, no, unknown
How did client know about the hotel chain? > 30 Categorical Internet, friends, others

Generated by the hotel chain Repeaters vs. first timers 2 Binary Yes or no
Cluster 1 done by the hotel Categorical
Cluster 2 done by the hotel Categorical
Cluster 3 done by the hotel Categorical

Transactional Data How was the customer registered? > 40 Categorical Cardex, call center
Entity in the last visit 8 Categorical Agency, B2B
Type of price applied to the customer 3 Categorical Promotion

P. Talón-Ballestero et al. Tourism Management 68 (2018) 187–197

189

more than once at the hotels in the chain) or first-timers and a client
clustering used by the chain.

Some studies have indicated that travelers with different national-
ities have different motivations for traveling (Kim & Lee, 2000; Kozak,
2002; Mok & Armstrong, 1998; Seddighi, Nuttall, & Theocharous,
2001) and that they may show differences in their behavioral char-
acteristics (Kim & Prideaux, 2005). Based on that, we focused on the
three nationalities with the largest number of clients in our chain,
namely, Germany, Spain, and the United Kingdom (UK), which re-
presented up to 70% of the total clients of this hotel chain (see Fig. 1).

3.2. Proportion tests and bootstrap resampling

In this study, we need to compare an overall high number of cate-
gories distributed among the features stored in the CRM system. We
limited our study to the most usual feature types in the CRM system,
i.e., the ones described either by dichotomous or by categorical values,
possibly (and very often) in the presence of missing values. We worked
with the whole set of categories in terms of two groups which are of our
special interest, i.e., repeaters (Group 1) vs first-timers (Group 2).

As an example, think of a simple feature, such as gender, for which
we have three possible values in the database, namely, female
(value = 0), male (value = 2) and unknown (value = 3). In this case,
we build a simple vector consisting of three elements, as described by
the following. If we denote by p g( )f 1 the female proportion in Group 1,
and by p g( )f 2 the female proportion of female in Group 2, we can define
the Category Proportion Difference (CPD) as

= −Δp p g p g( ) ( )f f f1 2 (1)

Accordingly, if (for example) our analysis yielded >Δp 0f , this
would indicate that this value of the feature should be more present in
Group 1 than in Group 2, and as far as Group 1 is repeater client, it
could be read as women are more prone to be repeater clients.

We can build the same statistical description for male and for un-
known values of the feature, namely, Δpm and Δpu. Note that these
proportions are quite related among them, nevertheless, we are scru-
tinizing explicitly all the possible values in terms of dichotomic cate-
gories. Note also that in the case of unknown or missing data, we should
read >Δp 0u as unknown gender is more prone to be present in the database
for repeater clients. Hence, for this example simple feature, we have a
three-dimensional vector of difference of proportions, which will be
denoted as

=v Δp Δp Δp[ , , ]gender f m u (2)

and it is called here the Feature Proportions Difference Vector (FPDV).
The FPDV quantification and visualization provides us with a clear,
exhaustive, and simple description of the trends in the values of this
feature, and its extension to categorical features is straightforward.

When the proportions of the i-th category are quite similar between
groups, their CPD, denoted from now as Δpi will be about zero. Nonzero
values will deserve special attention, as they are indicative of differ-
ences between both groups. At this point, we need to establish a simple-
to-use statistical test for determining whether a value of Δpi is large
enough to indicate a significant difference in this category between the
compared groups. Therefore, the statistical test that we need has the
following hypothesis:

1. Null hypothesis, H0: =Δp 0i , and there is no proportion difference
between groups for the i-th category.

2. Alternative hypothesis, H1: ≠Δp 0,i and there is a proportion differ-
ence between groups for the i-th category. If >Δp 0 (if <Δp 0), then the proportion of the i-th category is significantly larger in Group 1 (in Group 2).

This hypothesis test is depicted in Fig. 2. The described CPD hy-
pothesis test will be intensively used in large databases, as in the case of
our CRM features analysis. The proportion test for analyzing pi could be
dealt with simple statistical tests, however, it is well known that pro-
portion tests should be dealt with special caution (Sá, 2003), and fur-
thermore, the definition of parametric tests for Δpi can be sensitive in
classical statistics, for instance in terms of the actual assumed dis-
tributions. For these reasons, we propose using a new method for es-
timating clear cut-off hypothesis tests for CPD by using bootstrap re-
sampling, as described next in short.

The bootstrap resampling is a widely extended and common sta-
tistical technique which relies on random sampling with replacement
(also known as the plug-in principle) to provide statistical tests. The
plug-in principle says that the empirical distribution function of a sta-
tistic can be used as an approximation for the actual distribution
function (Efron & Tibshirani, 1994). The rationale of the bootstrap re-
sampling method is that if we want to make some inference of a po-
pulation in terms of some decision statistic whose calculation is known,
but its actual statistical distribution is not easy to obtain, we can re-
sample the population data and make the inference on the resample.

Fig. 3 shows the general approach to bootstrap resampling. In our
case, we want to provide a non-parametric hypothesis test for the CPD
statistic in each category in the database. Given a population, we
sample it with replacement several times B, which needs to be large
enough to represent the tails of the statistical distributions (typically
B = 50, 200, or 1000, depending on the application). These statistical
copies of the original population are known as bootstrap resamples. The
calculation of the decision statistic (in this case, the CPD for a category)
is made now on each resample, hence yielding a new value for it,

Fig. 1. Percentage of customers in terms of several of their nationalities.

Fig. 2. Hypothesis test for the i-th CPD.

P. Talón-Ballestero et al. Tourism Management 68 (2018) 187–197

190

known as bootstrap replication. The B replications of the statistics can be
used to build a histogram, which stands for an estimation of the actual
CPD probability density function, and then, simple methods, such as
ordered statistics, can be used to determine the hypothesis test. Note in
Fig. 3 and in the following that asterisk symbol (*) is used to identify
the quantities that have been estimated throughout the bootstrap pro-
cess from the original ones, these last often called the empirical statis-
tics.

3.3. Big data and map-reduce implementation

The increasing amount of data that is generated, along with the
need to extract useful knowledge from them, has created many pro-
blems in the last several years. These problems are mainly due to the
nature of these available data, since the tools used so far were not ap-
plicable to these data sets. In particular, these problems affect different
areas, such as hardware storage and accessibility of these data, database
management, pre-processing, simple analysis, or automatic extraction
of hidden patterns.

In order to address all these problems, a plethora of techniques have
emerged that allow, in a much more efficient way, to extract value from
data that were difficult to address, either by the large size of the
available database, or by its low quality, or for being a union of diverse
data sources with different nature. The framework encompassing this
whole set of tools and solutions tends to be referred to as Big Data. In
this article, we will use those tools that allow us to pre-process and
analyze large volumes of available data, since their size make it un-
feasible to analyze them using the classic tools. In particular, one of the
tools that can do this is known as map-reduce (Jeffrey & Ghemawat,
2008, pp. 107–113), an algorithmic framework that takes advantage of
both parallel hardware architecture and distributed file systems. In our
area of knowledge, one of the Big Data challenges aimed to extract
useful knowledge from large amounts of data is to create or adapt
statistical or machine learning techniques to the parallelizable frame-
work known as map-reduce. This map-reduce framework consists
mainly of two basic operations, map and reduce. The map function is
distributed and executed in parallel in the different computing nodes
that constitute the hardware architecture. The purpose of this map
function is to transform the available data format (possibly un-
structured) into a structured data form, which is known as key-value and
that allows us to solve the problem. The reduce function, also executed
in parallel, takes as input the outputs of a set of map functions and
summarizes all the key-value pairs belonging to the same key in a single
key-value pair. Therefore, the obtained result returns as many key-

value pairs as keys exist. Because the data has to be transformed into
this rigid key-value structure, not all problems seem to be resolved
efficiently with this map-reduce framework. In addition, most of the
algorithms that allow advanced data analysis (as are those in the ma-
chine learning field) are not easily parallelizable (embarrassingly par-
allel), and they require great effort to be implemented in the map-re-
duce form.

In this paper, we propose to apply a combination of classical sta-
tistical techniques (described in Subsection 3.2) that are embarrassingly
parallel in the map-reduce framework, in order to extract useful
knowledge from the large available data collection. For its im-
plementation, each bootstrap resampling block is distributed to each
available parallel computing node, and the proportion test is applied as
a map function. The union or sum of the outputs of each proportion test
constitutes the reduce function, thus obtaining the desired advanced
data analysis. Before this analysis, a necessary preprocessing is applied
through map-reduce procedure in order to prepare the appropriate
variables and the necessary information for the study.

4. Results

4.1. Some descriptive statistics

As previously described, data were recorded from 4,935,806 dif-
ferent clients in the CRM system during the years 2013 and 2014, and
the goal of this work consisted of modeling the profile of first-timers
and repeaters within this population, as it was found usual that many
clients stayed in the chain hotel several times. Towards that end, we
analyzed first-timers vs repeaters considering their nationality. The
whole analysis on the complete data set took about 15 min on a
MacBook Pro(R) (3.5 GHz, Intel Core i7), whereas it can be expected to
be reduced to just a few seconds when using instead a graphic pro-
cessing unit (GPU) or another computation server. For each of the se-
lected nationalities (Germany, the UK, and Spain), the age distribution
is represented in terms of the gender (men, women, and unknown) for
first-timers and repeaters in Fig. 4. Table 2 also summarizes the mean
and the standard deviation. In general, first-timer men were older than
repeaters, and the age for British citizen was lower than for the other
nationalities.

4.2. Advantages of big data hypothesis tests

Hypothesis tests based on probabilities are known to be sensitive in
terms of statistical robustness. This can be checked in Fig. 5(a), which

Fig. 3. Bootstrap resampling fundamentals for providing with a non-parametric hypothesis test for CPD in each category in the database.

P. Talón-Ballestero et al. Tourism Management 68 (2018) 187–197

191

represents the histograms estimated inside each map-reduce chunk for
the CPD statistic of gender feature (unknown value is not represented in
this example). Although the use of these statistical distributions for
hypothesis testing separately in each chunk would show a trend to
identify these variables as significant, there is still strong variability in
these distributions, so that they do not represent a good-quality re-
presentation of the probability density function of the CPD statistics.
This is surprising even given the large size of each chunk (200,000
cases).

However, Fig. 5(b) shows the aggregated estimation of the prob-
ability density functions from the accumulated averaged histograms
after each chunk. Whereas the first 10 chunks still provide a quite noisy
and fragmented density estimation, it soon becomes a smoother

estimation, and it reaches convergence (highly similar estimations)
after about half of the chunks have been included in the averaging. This
holds even while the distribution estimation is capturing subtle but
present multimodalities, which justifies the use of nonparametric sta-
tistical methods such as the bootstrap resampling. These observations
make evident that even simple statistics are efficiently dealt with Big
Data analytics (panel b) than with conventional approaches (panel a).

4.3. Chromosome and spiderweb results for proportion tests

We obtained a non-parametric hypothesis test for the CPD statistics
in each category in the database and for each nationality. Hypothesis
tests for the i-th CPD were calculated by using the bootstrap resampling

Fig. 4. Birth year distribution in terms of gender (men, women and unknown) for repeater and first-time visitors: (a) Germany; (b) UK; and (c) Spain.

P. Talón-Ballestero et al. Tourism Management 68 (2018) 187–197

192

technique for the proportion tests. Two different visualization techni-
ques were used for the large number of categories scrutinized in the Big
Database, namely, the so-called chromosome proportions plot and the
spiderweb representation. The first one shows all the proportion rates
for all the variables except for motivation due to visualization purposes
(more than 150 categories) in three different nationalities (Germany,
the UK, and Spain), as seen Fig. 6. All the values significantly higher
(lower) than 0 indicate that a significant difference exists between the
proportions of the i-th category for repeaters (first-timers). Red-dotted
lines are used as a threshold to identify the most relevant features with
a detailed and extensive description in terms of all the scrutinized ca-
tegories, so that the higher its absolute value, the more relevant that
feature is. We can see that interesting features often are viewed as
clusters or interest regions. Alternatively, a joint visualization for de-
fining the clients’ profile based on just the most relevant features are
represented by a spiderweb plot (see Fig. 7), which can provide the
managers with a coarse and rough view of the profile. Both re-
presentations are complementary and show different details to the
manager for analyzing the CRM features from a business point of view.

Several conclusions can be obtained after analyzing these two re-
presentations, almost at a glance. The clearest one (based on Fig. 6) is
that the profiles of German and British repeaters are similar to each
other and very different from that of the Spanish. In the case of German
and British repeaters, two groups of customers are important: clients
without children (category c85) and seniors with long stays (category
c73). In both cases, it is significant that they are single (category c149)
and travel in pairs (category c150). With respect to the first-timers
profile, and in the case of the British, the favorite destination is Mal-
lorca (category c167). Several fields with unknown value are sig-
nificant, such as civil status (category c148), customer segment (cate-
gory c39), customer group (category c71), or habitual residence
(category c400). Therefore, more information is required from this
group of clients in order to be able to better establish their specific

profile. However, Spanish repeaters belong to the young-client escape
group (category c78), traveling without children (category c85), and
with high frequency (category c55). It is also significant that they are
often single (category c149) and male (category c1). Among Spanish,
the first-timers profile is characterized by traveling as a family (cate-
gory c151) and booking through an agency (category c801).

5. Discussion and implications

The work in this paper allows us to scrutinize and present two

Table 2
Mean and standard deviation of birth year in terms of gender, for first-timers
and repeaters. First (second, third) row refers to German (Spanish, British)
customers.

Gender Nationality First-timers Repeaters

Men German

196

2.18 +- 14.64

195

6.80 +- 13.86
Spanish 1966.19 +- 14.46 1966.65 +- 12.61
British 1961.14 +- 16.26 1955.17 +- 14.55

Women German 1963.19 +- 15.28 1958.16 +- 14.48
Spanish 1968.32 +- 14.86 1970.31 +- 12.68
British 1962.88 +- 16.69 1957.39 +- 15.22

Unknown German 1960.88 +- 18.16 1956.02 +- 15.37
Spanish

194

9.85 +- 24.20 1964.12 +- 17.09
British 1963.92 +- 17.56 1957.62 +- 20.33

Fig. 5. Histograms yielding the use of hy-
pothesis testing for CPD statistic in an ex-
ample category (gender in Spain, men in
blue, women in pink): (a) Normalized his-
tograms estimated for each chunk in the
map-reduce procedure; (b) Aggregated
normalized histograms after each chunk in
the map-reduce procedure. (For interpreta-
tion of the references to colour in this figure
legend, the reader is referred to the Web
version of this article.)

Fig. 6. Chromosome proportions plots for Germany, UK, and Spain.

P. Talón-Ballestero et al. Tourism Management 68 (2018) 187–197

193

different types of implications, namely, the methodological issues with
respect to the application of the Big Data from CRM information, and
those derived from the hotel chain study concerning the profile of the
client. Both are summarized next after the discussion on the presented
results.

5.1. Discussion on the Big Data case study

In our study, the repeaters profile is more similar in German and
British guests than in Spanish guests. In the case of foreign repeaters,
they are characterized by being senior citizens traveling for long per-
iods without children. The importance of this segment in the tourism
market has already been pointed out, and its behavior has become of
great interest due to its size and potential growth (Chen, Liu, & Chang,
2013). Older consumers are presumed to have firmer brand loyalty than
younger consumers (Henry, 2000). Also, German and British first-
timers are significantly young clients on their holidays without children
in our data, similar to the results by Gitelson and Crompton (1984),
who noted that first-timers are more likely to be younger and single.

Spanish repeaters belong to the young-client escape group. These
results differ for Spanish clients and match for German and British
when compared with the results supported by Oppermann (1997), who
demonstrated that first-timers tend to spend more money, but they stay
for a shorter time than repeaters. Consistent findings were also ob-
served by Wang (2004) and Lau & Mckercher (2004), however, con-
tradictory findings regarding the length of stay were reported by Li
et al. (2008), who concluded that first-timers are most likely to stay for
longer periods, while repeaters are more likely to take weekend trips for
visiting friends and relatives. The latter issue is easier for Spanish than
for non-Spanish guests, since most of the chain establishments are lo-
cated in Spain and this makes it more accessible to national customers
to be able to move more frequently. This difference between the stay
duration of Spanish and non-Spanish guests has been previously
pointed out by Talón-Ballestero, González-Serrano, and Rodríguez-
Antón (2016), who showed that the length of stay of foreign clients is
longer in the Spanish hotel sector.

In addition, repeaters are single (especially for British and Spanish),
in contrast with the ideas of Tasci (2016), who concluded that married
clients were more loyal customers, and of Gitelson and Crompton
(1984), who highlighted that the first-timers were mostly single. The
fact of traveling without a family coincides in all three cases, which
stands for a remarkable result, since according to Talón-Ballestero et al.
(2016), up to 60% of the Spanish holiday market is families. Most of
them are men and, in the case of the Spanish, they hired their last B2C
visit. In making travel decisions, repeaters seem to rely more on their
own experiences than on other information sources, hence, they spend
much less time on planning (Li et al., 2008). On the other hand, Spanish
first-timers have significantly purchased their last visit through an
agency. According to Li et al. (2008), first-timers are more likely to rely
on advice from travel professionals for making their travel decisions.

Women in the three nationalities are less often repeaters con-
sistently in our data, which could be related to previous studies that

have shown the different behavior in men and women in terms of their
hotel preferences (Ariffin & Maghzi, 2012; Lutz & Ryan, 1993;
McCleary, 1994; Sammons, Moreo, Benson & Demico, 1999).

Interestingly, first-timers are less reported in the CRM systems, for
instance in terms of their civil status, among others. This brings into
view the need for as complete as possible data recording in order to
better identify customers and offer an adapted service, hence increasing
their willingness to repeat (Padilla-Melendez & Garrido-Moreno, 2013).

5.2. Technical implications

Our proposal paves the way towards a systematic yet simple ap-
proach to exploit the data from CRM systems through Big Data in order
to determine the client profile. This analysis has also shown the re-
markable effect of map-reduce techniques for yielding consistency to
probability based statistical tests, thus making it evident that even
simple statistical descriptions are more efficiently tackled than con-
ventional and lower-scale approaches. To our understanding, the most
relevant technical contribution of this work is to show that Big Data,
with simple statistical concepts and structured data available in the
organizations, yields a better knowledge of the client.

The data structure of CRMs is a strategic asset for companies and
their information is confidential, hence, CRM access for research pur-
poses can be possible through collaboration agreements between
companies and universities with shared interest to address Big Data
exploitation. This is a common situation in Data Science for companies,
and in our work, it has allowed us to obtain useful information from the
existing data structures in the organization. Nevertheless, the devel-
oped methodology can be completely replicated in different CRM da-
tabases by any practitioner or data scientist. In addition, our results
have provided useful information to the managers of the hotel chain
with a view to reconsider the data structure of the CRM and its possible
modification, and hence to improve its exploitation.

In this work, the clustering developed by the chain has been used as
a set of variables, but no additional client grouping has been generated
by our study, because our objective was to show the possibilities of Big
Data to exploit the information available in the CRM system by uni-
variate study of categorical variables. There is no doubt that other
advanced multivariable analytics can highlight interesting cross-re-
lationships among the business-relevant variables identified here by
simple proportion tests.

A detailed statistical study of convergence from the theoretical point
of view has not been carried out. But still, in the analyzed data, the
asymptotic behavior of the distributions has been empirically observed
(see Fig. 4) and this has been the expected one. A theoretical bootstrap
convergence study with large data is a future and interesting work that
exceeded our current objectives.

5.3. Hotel chain implications

Previous client classification studies based on statistical approaches
have been used with varying success, but they will not always be able of

Fig. 7. Spiderweb representations for
Germany (left), Spain (center) and UK
(right). Repeaters and first-timers profiles
are represented by red and yellow areas,
respectively. These areas are defined by the
| Δpi | for the most significant categories
above threshold.

P. Talón-Ballestero et al. Tourism Management 68 (2018) 187–197
194

dealing with very large data sets. The use of Big Data techniques has
allowed us to establish the repeaters vs first-timers profile in terms of
many features and categories. Our results show that nationality, gender,
age, length of stay, family conditions or selling channel, are strongly
relevant for this profiling in the studied hotel chain, and special at-
tention should be paid to their correct recording in the CRM system.

The more information recorded in the CRM systems, the easier it
will be to generate detailed profiles with simple Big Data techniques.
Hence, hotel managers will improve their client knowledge for in-
creasing satisfaction and loyalty, ensuring the repeating visit and the
increased profit (Tseng & Wu, 2014). From a marketing viewpoint,
these results can help to identify clusters of clients for guiding supply of
the company products and services in terms of their needs.

Moreover, it would be desirable to scrutinize the reorientation to-
wards the growing female segment (40,7% of the analyzed sample) and
to establish different actions in terms of the selling channel (b2c for
repeaters and agencies for first-timers). In addition, repeaters mostly
travel without children, which can support the current chain reor-
ientation towards non-family vacation segments and “adults only”
products.

One of the limitations of our study comes from the consideration of
a single hotel chain, and thus, of a specific CRM system. As pointed out,
access to these data requires collaboration agreements with companies.
Future research may use this methodology in different companies be-
cause the method can be easily applied to other CRM systems by Data
Scientists and experts.

Not only the conceptual approach, but also the practical guidance
for hospitality managers, can be usefully improved by our results. As it
has been observed, the correct management through the Big Data
techniques of the large volume of data generated by the clients during
their stay, which are regularly collected in the CRM system, allows a
greater and better knowledge of their characteristics, which will im-
prove the customer satisfaction, carry out personalized marketing
campaigns, as well as give offers to selected customers to book an
adequate room at the selected rate.

Future research lines can generalize the current results, for instance,
by comparing the differences between first-timers vs. repeaters for
different types of Hotel Chains, different purposes of visit (business
clients), and in different nationalities.

6. Final conclusions

We concluded that the repeater profile in this chain corresponds to
single, men, and traveling without children in the three scrutinized
nationalities, however, there are differences among nationalities in
terms of length of stay (larger in British and German than in Spanish)
and age (senior in British and German vs. younger in Spanish).
Moreover, due to the large number of tourists considered and the high
volume of their handled information, the profile detected in this chain
can be very useful not only to hotels, but also to tourist companies and
destinations, in order to conveniently adapt their products and their
marketing actions.

Overall, the great amount of available data from clients creates
relevant opportunities for the hotel companies, which can turn into a
strong competitive advantage. Further technical and more advanced
tools will allow us to better exploit the best available information about
the clients and their purchasing behavior. Our study has shown that
even simple statistics as the proportion tests can be used for stating a
solid large-scale information retrieval for client profiling, and it paves
the way towards Big Data approaches yielding strong support for de-
cision making of hotel managers.

Acknowledgements

This work has been partly supported by Spanish Projects
PRINCIPIAS (TEC2013-48439-C4-1-R), FINALE (TEC2016-75161-C2-1-

4), TEC2016-75361-R & ECO2016-75379-R from Spanish Government.

References

Adomavicius, G., & Tuzhilin, A. (2001, 03). Using data mining methods to build customer
profiles. Computer, 34(3), 74–82. http://dx.doi.org/10.1109/2.901170.

Anwar, S. A., & Sohail, M. S. (2004, 04). Festival tourism in the United Arab Emirates:
First-time versus repeat visitor perceptions. Journal of Vacation Marketing, 10(2),
161–170. http://dx.doi.org/10.1177/135676670401000206.

Ariffin, A. A., & Maghzi, A. (2012, 03). A preliminary study on customer expectations of
hotel hospitality: Influences of personal and hotel factors. International Journal of
Hospitality Management, 31(1), 191–198. http://dx.doi.org/10.1016/j.ijhm.2011.04.
012.

Balomenou, N., & Garrod, B. (2014, 10). Using volunteer-employed photography to in-
form tourism planning decisions: A study of St David’s peninsula, wales. Tourism
Management, 44, 126–139. http://dx.doi.org/10.1016/j.tourman.2014.02.015.

Bayer, J. (2010, 09). Customer segmentation in the telecommunications industry. The
Journal of Database Marketing & Customer Strategy Management, 17(3–4), 247–256.
http://dx.doi.org/10.1057/dbm.2010.21.

Buttle, F. (2004). Customer relationship management: Concepts and tools. Amsterdam:
Elsevier Butterworth-Heinemann.

Chadha, A. (2015, 03). Case study of hotel taj in the context of CRM and customer re-
tention. KCAJBMR Kuwait Chapter of Arabian Journal of Business and Management
Review, 4(7), 1–8. http://dx.doi.org/10.12816/0018976.

Chakravarti, D., & Day, G. S. (1991, 10). Market driven Strategy: Processes for creating
value. Journal of Marketing, 55(4), 116. http://dx.doi.org/10.2307/1251961.

Chen, K., Liu, H., & Chang, F. (2013, 12). Essential customer service factors and the
segmentation of older visitors within wellness tourism based on hot springs hotels.
International Journal of Hospitality Management, 35, 122–132. http://dx.doi.org/10.
1016/j.ijhm.2013.05.013.

Dev, C. S., & Olsen, M. D. (2000, 02). Marketing challenges for the next decade. Cornell
Hotel and Restaurant Administration Quarterly, 41(1), 41–47. http://dx.doi.org/10.
1177/001088040004100122.

Dursun, A., & Caber, M. (2016, 04). Using data mining techniques for profiling profitable
hotel customers: An application of RFM analysis. Tourism Management Perspectives,
18, 153–160. http://dx.doi.org/10.1016/j.tmp.2016.03.001.

Efron, B., & Tibshirani, R. (1994). An introduction to the bootstrap. New York: Chapman &
Hall.

Elfving, J., & Lemoine, K. (2012). Exploring the concept of customer relationship manage-
ment: Emphasizing socialPhd Master thesis. Supervisor: Karin Brunsson. Department of
Business Studies, Uppsala University 2012-05-25. .

Fallon, P., & Schofield, P. (2003, 01). First-timer versus repeat visitor Satisfaction: The
case of orlando, Florida. Tourism Analysis, 8(2), 205–210. http://dx.doi.org/10.3727/
108354203774076742.

Fluker, M. R., & Turner, L. W. (2000, 05). Needs, motivations, and expectations of a
commercial whitewater rafting experience. Journal of Travel Research, 38(4),
380–389. http://dx.doi.org/10.1177/004728750003800406.

Formica, S., & Uysal, M. (1998, 04). Market segmentation of an international cultural-
historical event in Italy. Journal of Travel Research, 36(4), 16–24. http://dx.doi.org/
10.1177/004728759803600402.

Fox, S., & Do, T. (2013, 09). Getting real about big Data: Applying critical realism to
analyse big data hype. International Journal of Managing Projects in Business, 6(4),
739–760. http://dx.doi.org/10.1108/ijmpb-08-2012-0049.

George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of
Management Journal, 57(2), 321–326.

Gitelson, R. J., & Crompton, J. L. (1984, 01). Insights into the repeat vacation phenom-
enon. Annals of Tourism Research, 11(2), 199–217. http://dx.doi.org/10.1016/0160-
7383(84)90070-7.

Haynes, N. (2016). The evolution of competitor data collection in the hotel industry and
its application to revenue management and pricing. Journal of Revenue and Pricing
Management, 15(3–4), 258–263.

Hendricks, K. B., Singhal, V. R., & Stratman, J. K. (2007). The impact of enterprise sys-
tems on corporate performance: A study of ERP, SCM, and CRM system im-
plementations. Journal. Henry, C. D. (2000, 07). Is customer loyalty a pernicious
myth? Business Horizons, 43(4), 13–16. doi:10.1016/s0007-6813(00)00066-5.

Henry, C. D. (2000). Is customer loyalty a pernicious myth? Business Horizons, 43(4),
http://dx.doi.org/10.1016/S0007-6813(00)00066-5 13–13.

Hjorth, L. (2012, 12). Relocating the mobile: A case study of locative media in seoul,
South Korea. Convergence: The International Journal of Research Into New Media
Technologies, 19(2), 237–249. http://dx.doi.org/10.1177/1354856512462360.

Homburg, C., & Giering, A. (2000). Personal characteristics as moderators of the re-
lationship between customer satisfaction and loyalty?an empirical analysis.
Psychology and Marketing, 18(1), 43–66. http://dx.doi.org/10.1002/1520-
6793(200101)18:13.0.co;2-i.

Jeffrey, D., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters.
Commun. ACM 51, 1 (January 2008) https://doi.org/10.1145/1327452.1327492.

Abu Kasim, N. A., & Minai, B. (2009). Linking CRM strategy, customer performance
measures and performance in the hotel industry. International Journal of Economics
and Management, 3(2), 297–316.

Katz, H. (2002). How to embrace CRM and make it succeed in an organization. SYSPRO
white paper. Costa Mesa, CA: SYSPRO.

Keramati, A., Mehrabi, H., & Mojir, N. (2010, 10). A process-oriented perspective on
customer relationship management and organizational performance: An empirical
investigation. Industrial Marketing Management, 39(7), 1170–1185. http://dx.doi.org/
10.1016/j.indmarman.2010.02.001.

P. Talón-Ballestero et al. Tourism Management 68 (2018) 187–197
195

http://dx.doi.org/10.1109/2.901170

http://dx.doi.org/10.1177/135676670401000206

http://dx.doi.org/10.1016/j.ijhm.2011.04.012

http://dx.doi.org/10.1016/j.ijhm.2011.04.012

http://dx.doi.org/10.1016/j.tourman.2014.02.015

http://dx.doi.org/10.1057/dbm.2010.21

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref6

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref6

http://dx.doi.org/10.12816/0018976

http://dx.doi.org/10.2307/1251961

http://dx.doi.org/10.1016/j.ijhm.2013.05.013

http://dx.doi.org/10.1016/j.ijhm.2013.05.013

http://dx.doi.org/10.1177/001088040004100122

http://dx.doi.org/10.1177/001088040004100122

http://dx.doi.org/10.1016/j.tmp.2016.03.001

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref12

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref12

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref13

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref13

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref13

http://dx.doi.org/10.3727/108354203774076742

http://dx.doi.org/10.3727/108354203774076742

http://dx.doi.org/10.1177/004728750003800406

http://dx.doi.org/10.1177/004728759803600402

http://dx.doi.org/10.1177/004728759803600402

http://dx.doi.org/10.1108/ijmpb-08-2012-0049

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref18

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref18

http://dx.doi.org/10.1016/0160-7383(84)90070-7

http://dx.doi.org/10.1016/0160-7383(84)90070-7

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref20

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref20

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref20

http://dx.doi.org/10.1016/S0007-6813(00)00066-5

http://dx.doi.org/10.1177/1354856512462360

http://dx.doi.org/10.1002/1520-6793(200101)18:13.0.co;2-i

http://dx.doi.org/10.1002/1520-6793(200101)18:13.0.co;2-i

https://doi.org/10.1145/1327452.1327492

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref26

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref26

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref26

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref27

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref27

http://dx.doi.org/10.1016/j.indmarman.2010.02.001

http://dx.doi.org/10.1016/j.indmarman.2010.02.001

Kim, B., & Choi, J. P. (2010, 06). Customer information Sharing: Strategic incentives and
new implications. Journal of Economics and Management Strategy, 19(2), 403–433.
http://dx.doi.org/10.1111/j.1530-9134.2010.00256.x.

Kim, S., Jung, T., Suh, E., & Hwang, H. (2006, 07). Customer segmentation and strategy
development based on customer lifetime value: A case study. Expert Systems with
Applications, 31(1), 101–107. http://dx.doi.org/10.1016/j.eswa.2005.09.004.

Kim, M., Knutson, B. J., & Vogt, C. A. (2014). Posttrip behavioral differences between
first-time and repeat guests: A two-phase study in a hospitality setting. Journal of
Hospitality Marketing & Management, 23(7), 722–745. http://dx.doi.org/10.1080/
19368623.2014.891960.

Kim, C., & Lee, S. (2000, 07). Understanding the cultural differences in tourist motivation
between anglo-american and Japanese tourists. Journal of Travel & Tourism Marketing,
9(1–2), 153–170. http://dx.doi.org/10.1300/j073v09n01_09.

Kim, S. S., & Prideaux, B. (2005, 06). Marketing implications arising from a comparative
study of international pleasure tourist motivations and other travel-related char-
acteristics of visitors to Korea. Tourism Management, 26(3), 347–357. http://dx.doi.
org/10.1016/j.tourman.2003.09.022.

Kotler, P. (2002). When to use CRM and when to forget it!. Paper presented at the academy
of marketing science Sanibel Harbour Resort and Spa, 30 May.

Kozak, M. (2002, 06). Comparative analysis of tourist motivations by nationality and
destinations. Tourism Management, 23(3), 221–232. http://dx.doi.org/10.1016/
s0261-5177(01)00090-5.

Lau, A. L., & Mckercher, B. (2004, 02). Exploration versus acquisition: A comparison of
first-time and repeat visitors. Journal of Travel Research, 42(3), 279–285. http://dx.
doi.org/10.1177/0047287503257502.

Lee, S., Hwang, E., Jo, J. Y., & Kim, Y. (2016). Big data analysis with hadoop on perso-
nalized incentive model with statistical hotel customer data. International Journal of
Software Innovation, 4(3), 1–21.

Li, X., Cheng, C., Kim, H., & Petrick, J. F. (2008, 04). A systematic comparison of first-
time and repeat visitors via a two-phase online survey. Tourism Management, 29(2),
278–293. http://dx.doi.org/10.1016/j.tourman.2007.03.010.

Lin, Y., & Su, H. (2003, 08). Strategic analysis of customer relationship management—a
field study on hotel enterprises. Total Quality Management and Business Excellence,
14(6), 715–731. http://dx.doi.org/10.1080/1478336032000053843.

Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008, 06). Electronic word-of-mouth in hos-
pitality and tourism management. Tourism Management, 29(3), 458–468. http://dx.
doi.org/10.1016/j.tourman.2007.05.011.

Lutz, J., & Ryan, C. (1993, 10). Hotels and the businesswoman. Tourism Management,
14(5), 349–356. http://dx.doi.org/10.1016/0261-5177(93)90003-4.

Mccleary, K. (1994, 04). Gender-based differences in business travelers’ lodging pre-
ferences. Cornell Hotel and Restaurant Administration Quarterly, 35(2), 51–58. http://
dx.doi.org/10.1016/0010-8804(94)90019-1.

Mckercher, B. (2004, 11). Understanding tourism Behavior: Examining the combined
effects of prior visitation history and destination status. Journal of Travel Research,
43(2), 171–179. http://dx.doi.org/10.1177/0047287504268246.

Min, H., Min, H., & Emam, A. (2002, 11). A data mining approach to developing the
profiles of hotel customers. International Journal of Contemporary Hospitality
Management, 14(6), 274–285. http://dx.doi.org/10.1108/09596110210436814.

Mok, C., & Armstrong, R. W. (1998, 10). Expectations for hotel service quality: Do they
differ from culture to culture? Journal of Vacation Marketing, 4(4), 381–391. http://
dx.doi.org/10.1177/135676679800400406.

Morais, D. B., & Lin, C. (2010, 03). Why do first-time and repeat visitors patronize a
destination? Journal of Travel & Tourism Marketing, 27(2), 193–210. http://dx.doi.
org/10.1080/10548401003590443.

Nasution, H. N., & Mavondo, F. T. (2008, 04). Organisational capabilities: Antecedents
and implications for customer value. European Journal of Marketing, 42(3/4),
477–501. http://dx.doi.org/10.1108/03090560810853020.

Nguyen, T. H., Sherif, J. S., & Newby, M. (2007, 05). Strategies for successful CRM im-
plementation. Information Management & Computer Security, 15(2), 102–115. http://
dx.doi.org/10.1108/09685220710748001.

Noone, B. M. (2016). Pricing for hotel revenue management: Evolution in an era of price
transparency. Journal of Revenue and Pricing Management, 15(3–4), 264–269.

Oppermann, M. (1997, 05). First-time and repeat visitors to New Zealand. Tourism
Management, 18(3), 177–181. http://dx.doi.org/10.1016/s0261-5177(96)00119-7.

Özköse, H., Arı, E. S., & Gencer, C. (2015). Yesterday, today and tomorrow of big data.
Procedia-Social and Behavioral Sciences, 195, 1042–1050.

Padilla-Meléndez, A., & Garrido-Moreno, A. (2013, 06). Customer relationship manage-
ment in hotels: Examining critical success factors. Current Issues in Tourism, 17(5),
387–396. http://dx.doi.org/10.1080/13683500.2013.805734.

Pan, B., & Li, X. R. (2011). The long tail of destination image and online marketing. Annals
of Tourism Research, 38(1), 132–152.

Pan, B. I. N. G., & Yang, Y. A. N. G. (2016). Monitoring and forecasting tourist activities
with Big Data. Management Science in Hospitality and Tourism: Theory, Practice, and
Applications, 43.

Papastathopoulou, P., Avlonitis, G. J., & Panagopoulos, N. G. (2007, 04).
Intraorganizational information and communication technology diffusion:
Implications for industrial sellers and buyers. Industrial Marketing Management, 36(3),
322–336. http://dx.doi.org/10.1016/j.indmarman.2005.10.002.

Payne, A., & Frow, P. (2005, 10). A strategic framework for customer relationship
management. Journal of Marketing, 69(4), 167–176. http://dx.doi.org/10.1509/jmkg.
2005.69.4.167.

Peter, T. (2014). Use hotel data to drive growth. http://www.hotelnewsnow.com/Article/
14553/Use-hotel-data-to-drive-growth, accessed 26/01/2016.

Petrick, J. F. (2004, 08). Are loyal visitors desired visitors? Tourism Management, 25(4),
463–470. http://dx.doi.org/10.1016/s0261-5177(03)00116-x.

Petrick, J. F. (2005, 10). Segmenting cruise passengers with price sensitivity. Tourism

Management, 26(5), 753–762. http://dx.doi.org/10.1016/j.tourman.2004.03.015.
Ramos, C. M., Martins, D. J., Serra, F., Lam, R., Cardoso, P. J., Correia, M. B., et al. (2017).

Framework for a hospitality big data Warehouse: The implementation of an efficient
hospitality business intelligence system. International Journal of Information Systems in
the Service Sector, 9(2), 27–45.

Rong, J., Vu, H. Q., Law, R., & Li, G. (2012, 08). A behavioral analysis of web sharers and
browsers in Hong Kong using targeted association rule mining. Tourism Management,
33(4), 731–740. http://dx.doi.org/10.1016/j.tourman.2011.08.006.

Ryu, K., & Han, H. (2011, 09). New or repeat customers: How does physical environment
influence their restaurant experience? International Journal of Hospitality Management,
30(3), 599–611. http://dx.doi.org/10.1016/j.ijhm.2010.11.004.

Sammons, G., Moreo, P., Benson, L. F., & Demicco, F. (1999, 05). Analysis of female
business travelers’ selection of lodging accommodations. Journal of Travel & Tourism
Marketing, 8(1), 65–83. http://dx.doi.org/10.1300/j073v08n01_04.

Santana-Cerdeña, L., Ramos, S., & Bobur, S. (2014). Potential y Retos del Big Data en
Turismo. X Congreso de Turismo y Tecnologías de la Información y las Comunicaciones
(pp. 21–35). Universidad de Málaga, Facultad de Turismo.

Sarmaniotis, C., Assimakopoulos, C., & Papaioannou, E. (2013, 07). Successful im-
plementation of CRM in luxury hotels: Determinants and measurements. EuroMed
Journal of Business, 8(2), 134–153. http://dx.doi.org/10.1108/emjb-06-2013-0031.

Sá, J. P. Marques De (2003). Applied Statistics: Using SPSS, STATISTICA, and MATLAB.
Berlin: Springer.

Seddighi, H., Nuttall, M., & Theocharous, A. (2001, 04). Does cultural background of
tourists influence the destination choice? an empirical study with special reference to
political instability. Tourism Management, 22(2), 181–191. http://dx.doi.org/10.
1016/s0261-5177(00)00046-7.

Sigala, M. (2005, 09). Integrating customer relationship management in hotel operations:
Managerial and operational implications. International Journal of Hospitality
Management, 24(3), 391–413. http://dx.doi.org/10.1016/j.ijhm.2004.08.008.

Silva, A. P., & Mateus, G. R. (2003, 05). A location-based service application for a mobile
computing environment. SIMULATION, 79(5–6), 343–360. http://dx.doi.org/10.
1177/0037549703037151.

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017, 01). Critical analysis of
Big Data challenges and analytical methods. Journal of Business Research, 70,
263–286. http://dx.doi.org/10.1016/j.jbusres.2016.08.001.

Skogland, I., & Siguaw, J. A. (2004, 08). Are your satisfied customers loyal? Cornell Hotel
and Restaurant Administration Quarterly, 45(3), 221–234. http://dx.doi.org/10.1177/
0010880404265231.

Soltani, Z., & Navimipour, N. J. (2016, 08). Customer relationship management me-
chanisms: A systematic review of the state of the art literature and recommendations
for future research. Computers in Human Behavior, 61, 667–688. http://dx.doi.org/10.
1016/j.chb.2016.03.008.

Song, H., & Liu, H. (2017). Predicting tourist demand using big data. Analytics in smart
tourism design (pp. 13–29). Springer International Publishing.

Talón-Ballestero, P., González-Serrano, L., & Rodríguez-Antón, J. M. (2016). Fundamentos
de Dirección Hotelera, Vol. IMadrid: Síntesis978-84-9077-392-5.

Tasci, A. D. (2016). A quest for destination loyalty by profiling loyal travelers. Journal of
Destination Marketing & Management. https://doi.org/10.1016/j.jdmm.2016.04.001.

Tseng, S., & Wu, P. (2014, 03). The impact of customer knowledge and customer re-
lationship management on service quality. International Journal of Quality and Service
Sciences, 6(1), 77–96. http://dx.doi.org/10.1108/ijqss-08-2012-0014.

Tseng, C., Wu, B., Morrison, A. M., Zhang, J., & Chen, Y. (2015, 02). Travel blogs on China
as a destination image formation agent: A qualitative analysis using leximancer.
Tourism Management, 46, 347–358. http://dx.doi.org/10.1016/j.tourman.2014.07.
012.

Verdugo, C. M., Oviedo-Garcia, A. M., & Roldan, L. J. (2009). The employee-customer
relationship quality: Antecedents and consequences in the hotel industry.
International Journal of Contemporary Hospitality Management, 21(3), 251–274.

Vu, H. Q., Li, G., Law, R., & Ye, B. H. (2015, 02). Exploring the travel behaviors of
inbound tourists to Hong Kong using geotagged photos. Tourism Management, 46,
222–232. http://dx.doi.org/10.1016/j.tourman.2014.07.003.

Wang, D. (2004, 02). Tourist behaviour and repeat Visitation to Hong Kong. Tourism
Geographies, 6(1), 99–118. http://dx.doi.org/10.1080/14616680320001722355.

Wind, Y., & Lerner, D. (1979, 02). On the measurement of purchase Data: Surveys versus
purchase diaries. Journal of Marketing Research, 16(1), 39. http://dx.doi.org/10.
2307/3150872.

Wu, S., & Li, P. (2011, 06). The relationships between CRM, RQ, and CLV based on dif-
ferent hotel preferences. International Journal of Hospitality Management, 30(2),
262–271. http://dx.doi.org/10.1016/j.ijhm.2010.09.011.

Wu, S., & Lu, C. (2012, 03). The relationship between CRM, RM, and business perfor-
mance: A study of the hotel industry in taiwan. International Journal of Hospitality
Management, 31(1), 276–285. http://dx.doi.org/10.1016/j.ijhm.2011.06.012.

Xiang, Z., Schwartz, Z., Gerdes, J. H., & Uysal, M. (2015). What can Big Data and text
analytics tell us about hotel guest experience and satisfaction? International Journal of
Hospitality Management, 44, 120–130.

Yang, Y., Pan, B., & Song, H. (2014). Predicting hotel demand using destination mar-
keting organization’s web traffic data. Journal of Travel Research, 53(4), 433–447.

Ye, Q., Law, R., & Gu, B. (2009). The impact of online user reviews on hotel room sales.
International Journal of Hospitality Management, 28(1), 180–182.

Yoo, M., & Bai, B. (2013, 06). Customer loyalty marketing research: A comparative ap-
proach between hospitality and business journals. International Journal of Hospitality
Management, 33, 166–177. http://dx.doi.org/10.1016/j.ijhm.2012.07.009.

Zhang, Y., Shu, S., Ji, Z., & Wang, Y. (2015, March). A study of the commercial appli-
cation of big data of the international hotel group in China: Based on the case study of
marriott international. Big data computing service and applications (pp. 412–417). .

P. Talón-Ballestero et al. Tourism Management 68 (2018) 187–197
196

http://dx.doi.org/10.1111/j.1530-9134.2010.00256.x

http://dx.doi.org/10.1016/j.eswa.2005.09.004

http://dx.doi.org/10.1080/19368623.2014.891960

http://dx.doi.org/10.1080/19368623.2014.891960

http://dx.doi.org/10.1300/j073v09n01_09

http://dx.doi.org/10.1016/j.tourman.2003.09.022

http://dx.doi.org/10.1016/j.tourman.2003.09.022

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref34

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref34

http://dx.doi.org/10.1016/s0261-5177(01)00090-5

http://dx.doi.org/10.1016/s0261-5177(01)00090-5

http://dx.doi.org/10.1177/0047287503257502

http://dx.doi.org/10.1177/0047287503257502

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref37

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref37

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref37

http://dx.doi.org/10.1016/j.tourman.2007.03.010

http://dx.doi.org/10.1080/1478336032000053843

http://dx.doi.org/10.1016/j.tourman.2007.05.011

http://dx.doi.org/10.1016/j.tourman.2007.05.011

http://dx.doi.org/10.1016/0261-5177(93)90003-4

http://dx.doi.org/10.1016/0010-8804(94)90019-1

http://dx.doi.org/10.1016/0010-8804(94)90019-1

http://dx.doi.org/10.1177/0047287504268246

http://dx.doi.org/10.1108/09596110210436814

http://dx.doi.org/10.1177/135676679800400406

http://dx.doi.org/10.1177/135676679800400406

http://dx.doi.org/10.1080/10548401003590443

http://dx.doi.org/10.1080/10548401003590443

http://dx.doi.org/10.1108/03090560810853020

http://dx.doi.org/10.1108/09685220710748001

http://dx.doi.org/10.1108/09685220710748001

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref49

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref49

http://dx.doi.org/10.1016/s0261-5177(96)00119-7

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref51

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref51

http://dx.doi.org/10.1080/13683500.2013.805734

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref53

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref53

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref54

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref54

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref54

http://dx.doi.org/10.1016/j.indmarman.2005.10.002

http://dx.doi.org/10.1509/jmkg.2005.69.4.167

http://dx.doi.org/10.1509/jmkg.2005.69.4.167

http://www.hotelnewsnow.com/Article/14553/Use-hotel-data-to-drive-growth

http://www.hotelnewsnow.com/Article/14553/Use-hotel-data-to-drive-growth

http://dx.doi.org/10.1016/s0261-5177(03)00116-x

http://dx.doi.org/10.1016/j.tourman.2004.03.015

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref60

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref60

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref60

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref60

http://dx.doi.org/10.1016/j.tourman.2011.08.006

http://dx.doi.org/10.1016/j.ijhm.2010.11.004

http://dx.doi.org/10.1300/j073v08n01_04

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref64

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref64

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref64

http://dx.doi.org/10.1108/emjb-06-2013-0031

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref66

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref66

http://dx.doi.org/10.1016/s0261-5177(00)00046-7

http://dx.doi.org/10.1016/s0261-5177(00)00046-7

http://dx.doi.org/10.1016/j.ijhm.2004.08.008

http://dx.doi.org/10.1177/0037549703037151

http://dx.doi.org/10.1177/0037549703037151

http://dx.doi.org/10.1016/j.jbusres.2016.08.001

http://dx.doi.org/10.1177/0010880404265231

http://dx.doi.org/10.1177/0010880404265231

http://dx.doi.org/10.1016/j.chb.2016.03.008

http://dx.doi.org/10.1016/j.chb.2016.03.008

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref73

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref73

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref74

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref74

https://doi.org/10.1016/j.jdmm.2016.04.001

http://dx.doi.org/10.1108/ijqss-08-2012-0014

http://dx.doi.org/10.1016/j.tourman.2014.07.012

http://dx.doi.org/10.1016/j.tourman.2014.07.012

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref78

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref78

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref78

http://dx.doi.org/10.1016/j.tourman.2014.07.003

http://dx.doi.org/10.1080/14616680320001722355

http://dx.doi.org/10.2307/3150872

http://dx.doi.org/10.2307/3150872

http://dx.doi.org/10.1016/j.ijhm.2010.09.011

http://dx.doi.org/10.1016/j.ijhm.2011.06.012

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref84

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref84

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref84

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref85

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref85

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref86

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref86

http://dx.doi.org/10.1016/j.ijhm.2012.07.009

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref88

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref88

http://refhub.elsevier.com/S0261-5177(18)30067-0/sref88

Pilar Talón-Ballestero is a PhD holder in Advanced
Marketing, Associate Professor in Business Economics
Department and Director the university Master in Revenue
Management of Rey Juan Carlos University. She is the
Director and researcher in numerous studies and con-
sultancy projects developed with private and public enti-
ties: Spanish Confederation of Hotels and Touristic
Acommodations, Spanish Hotel Technological Institute,
Iberostar, Nh and Palladium Hotels groups, etc. She has
published numerous publications (articles and books), in-
cluding some impact ones about hotel sector. She was hotel
and travel agency manager. Her areas of specialization are
revenue management, distribution, big data and gender in

tourism.

Lydia González-Serrano is a PhD holder in Economics and
Business Administration (Finance) and Associate Professor
in BusinessEconomics Department of Rey Juan Carlos
University. She is the Director and researcher in numerous
research and consultancy projects developed with private
and public entities: Institute for Women, Spanish Agency
for International Cooperation and Development, Spanish
Confederation of hotels and Touristic Acommodations,
Spanish Technological Institute, etc. Her research activity
has been reflected in numerous publications, including
some impact ones. Her several books about hotel manage-
ment have been published. Research lines are focused on
two key issues: finance and risk analysis and hotel man-
agement.

Cristina Soguero-Ruiz received the Telecommunication
Engineering degree, the B.Sc. degree in Business
Administration and Management, and the M.Sc. degree in
Biomedical Engineering from the University Rey Juan
Carlos, Madrid, Spain, in 2011 and 2012. She got the Ph.D.
degree in Machine Learning with Applications in
Healthcare in 2015 in the Joint Doctoral Program in
Multimedia and Communications in conjunction with
University Rey Juan Carlos and University Carlos III. She
was supported by FPU Spanish Research and Teaching
Fellowship (granted in 2012). She won the Orange
Foundation Best PhD Thesis Award by the Spanish Official
College of Telecommunication Engineering.

Sergio Muñoz-Romero earned his PhD in Machine
Learning at Universidad Carlos III de Madrid, where he also
received the Telecommunication Engineering Degree. He
has led pioneering projects where machine learning
knowledge was successfully used to solve real Big Data
problems. Currently, he is a researcher at Universidad Rey
Juan Carlos. Since 2015, he has worked at Persei vivarium
as Head of Data Science and Big Data. His present research
interests are centered in machine learning algorithms and
Statistical Learning Theory, mainly in dimensionality re-
duction and feature selection methods, and their applica-
tions to Big Data. The whole team have developed the same
amount of work. The engineers (JLRA, CSR, and SMR) fo-

cused on the development of the analysis tools, programmed the big data methods, parsed
the raw data and generated the results. Business management members (PTB and LGS)
proposed the idea of the big data application on CRM and its linkage with hotel sector
management. All the authors contributed to write the abstract, introduction, results and
discussion-conclusion sections. PTB and LGS elaborated Section 2, and SMR, CSR, and
JLRA elaborated Section 3.

José Luis Rojo-Álvarez received the Telecommunication
Engineering Degree in 1996 from University of Vigo, Spain,
and the PhD in Telecommunication Engineering in 2000
from the Polytechnic University of Madrid, Spain. Since
2016, he has been a Full Professor in the Department of
Signal Theory and Communications, University Rey Juan
Carlos, Madrid, Spain. He has published more than 100
indexed papers and more than 170 international conference
communications. He has participated in more than 60
projects (with public and private fundings) and directed
more than 10 of them. In 2016 he received the Rey Juan
Carlos University Price to Talented Researcher.

P. Talón-Ballestero et al. Tourism Management 68 (2018) 187–197
197

  • Using big data from Customer Relationship Management information systems to determine the client profile in the hotel sector
  • Introduction
    Literature review
    CRM in the hospitality industry
    Big data in the hospitality industry
    Client profiling in first-timers vs repeaters
    Statistical and data analytics methods
    Big database
    Proportion tests and bootstrap resampling
    Big data and map-reduce implementation
    Results
    Some descriptive statistics
    Advantages of big data hypothesis tests
    Chromosome and spiderweb results for proportion tests
    Discussion and implications
    Discussion on the Big Data case study
    Technical implications
    Hotel chain implications
    Final conclusions
    Acknowledgements
    References

Contents lists available at ScienceDirect

Resources, Conservation & Recycling

journal homepage: www.elsevier.com/locate/resconrec

Full length article

T

ire forward and reverse supply chain design considering customer
relationship management

Maedeh Yadollahiniaa, Ebrahim Teimourya, Mohammad Mahdi Paydarb,⁎

a School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
b Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran

A R T I C L E I N F O

Keywords:
Supply chain network design
Customer relationship management
Uncertainty
Tire
Robust optimization
Multi-choice goal programming

A B S T R A C T

During the last decade, reverse logistics networks have grown dramatically within many supply chains in dif-
ferent industries. Several evolving factors including economic climate, green image, environment protection
laws and social respolities force companies to revise their strategies. In this paper, a tire forward and reverse
supply chain is designed, and a multi-objective, multi-period, multi-product mixed integer linear programming
model considering uncertainty is developed. Moreover, a novel idea of integrating customer relationship man-
agement concept and supply chain management is proposed and incorporated into the mathematical modeling
framework. The proposed scenario-based multi-objective model is then solved following robust optimization and
revised multi-choice goal programming approaches. In order to discuss the managerial implications of the model
and its results, the realization rates of the objectives, considering their importance to the supply chain, are
illustrated. The model is implemented in LINGO 9 software package and solved utilizing the branch-and-bound
method. The results demonstrate the applicability of the model in real world situations.

1. Introduction

Statistics show that over 17 million tons of scrap tires are generated
annually all over the world (Simic and Dabic-Ostojic, 2016). Used tire

s

contain hazardous materials that can threaten human health, pollute air
and water resources and endanger life on Earth; therefore, end-of-life
tires can cause serious problems if not considered properly (Subulan
et al., 2015). In addition, some critical factors such as government
regulations, economic issues, increscent customer awareness and social
responsibilities have made both academia and industrial practitioners
pay a special attention to finding out best ways to deal with the problem
of used tires in recent years.

Proper supply chain management (SCM) can be a solution to the
problem of planning scrap tires. Due to the fact that there are numerous
forward supply chains (SCs) currently working worldwide, it will be
much more valuable and practical if the planning considers the fact that
some elements of SCs are now working and need to be optimized while
other elements need to be designed and integrated with the existing
ones. Reverse logistics, as opposed to the traditional forward logistics, is
the collection of all activities that deliver wastes or end-of-use/life
products that are no longer needed by users to special facilities for
further recovery or environmentally conscious disposal (Fleischmann
et al., 1997).

One of the main challenges in reverse logistics, especially in the case
of used tires, is the absence of a systematic procedure to collect end-of-
life products for the aim of recycling (de Souza and D’Agosto, 2013).
Legislating much more rigid regulations might be the first and easiest
answer to this problem. However, even with enforcing regulations, an
efficient strategy for the supply chain to collect used tires must be de-
vised in order to help businesses survive the competitive market. A
well-known tool that has brought competitive advantages to many
companies is customer relationship management (CRM), which is
shown to be effective if incorporated into the SCM planning and deci-
sion-making process.

Integrating the SCM and CRM enables companies to achieve a
number of improvements in their financial and performance metrics.
However, most companies rarely consider these two concepts simulta-
neously (Kracklauer et al., 2004). Liu and You (2011) stated that the
CRM is approached from a classical point of view and needs to be
considered through SCs.

Several definitions are proposed for the CRM in the literature and
the best clarified ones are as follows. The CRM is a concept that tries to
develop a relationship by considering two important factors of mar-
keting and customers (Kotler and Keller, 2012). It is a communicating
procedure between an organization’s service and its customers in order
to attract and also keep the organization’s truthful customers

https://doi.org/10.1016/j.resconrec.2018.07.018
Received 23 July 2017; Received in revised form 8 July 2018; Accepted 15 July 2018

⁎ Corresponding author.
E-mail addresses: m_yadollahinia@yahoo.com (M. Yadollahinia), Teimoury@iust.ac.ir (E. Teimoury), paydar@nit.ac.ir (M.M. Paydar).

Resources, Conservation & Recycling 138 (2018) 215–

228

0921-3449/ © 2018 Elsevier B.V. All rights reserved.

T

http://www.sciencedirect.com/science/journal/09213449

https://www.elsevier.com/locate/resconrec

https://doi.org/10.1016/j.resconrec.2018.07.018

https://doi.org/10.1016/j.resconrec.2018.07.018

mailto:m_yadollahinia@yahoo.com

mailto:Teimoury@iust.ac.ir

mailto:paydar@nit.ac.ir

https://doi.org/10.1016/j.resconrec.2018.07.018

http://crossmark.crossref.org/dialog/?doi=10.1016/j.resconrec.2018.07.018&domain=pdf

(Grönroos, 2000). It could be said that the CRM is to introduce the right
product to the right customer at the right time through the right
channel for the aim of fulfilling the customer’s extending demand.

The CRM needs a customer-centered business philosophy and a
culture that supports impressive service processes, marketing and sales.
The point in the CRM is to establish enduring and mutually useful re-
lationships, in which the seller and buyer aim to develop satisfying
interchanges. Discovering new customers and keeping existing ones
informed, engaged and loyal play an important role in the CRM.

One of the main challenges in this article is to incorporate the
concept of the CRM into strategic and tactical SCM decisions; specifi-
cally, network design as a strategic decision is of interest due to the
following reasons. First, a good network design enables a company to
have effective and efficient relationships with its customers. This, in
turn, facilitates activities of the company to recognize and fulfill real
needs of customers, which results in an atmosphere in which customers
are eager to buy the company’s products in the forward flow for the first
time. These customers are initially considered as temporary buyers. By
concentrating on CRM strategies, these temporary customers can be-
come the company’s loyal/key customers through time. Second, with
the CRM in mind, new action plans can be defined for the aim of
maximizing customer satisfaction, which can help to motivate custo-
mers to cooperate with the SC in collecting end-of-use products in the
reverse flow. Third, considering customer requirement, which means
enhancing customer value, can differentiate the SC in the global com-
petitive marketplace, resulting in revenue growth.

Another challenge in reverse logistics of used tires is to find best
ways to process end-of-life products. The first and easiest way might be
disposing used tires in landfills. However, this traditional method of
waste treatment is subject to several known drawbacks and risks. Used
tires are almost non-degradable and occupy considerable landfill
spaces. Increasing global environmental awareness and limited disposal
space have led researchers to seek alternative ways to deal with used
tires, some of which are

as follows:

1 Retread: Retreading involves removing the outside or tread of the
tire and adding a new tread.

2 Tire-derived fuel: One of the special features of materials used in
tires is their high heating value. Therefore, it is beneficial to use
scrap tires as fuel. Although it is not recycling indeed, it is preferred
to landfilling. Since scrap tires could be utilized in much more useful
ways, this approach is not very common.

3 Pyrolysis: Chemical decomposition of the tire by high heat under the
restrained condition is called pyrolysis, which can result in carbon
black, oil and steel. Although the mentioned process is totally sci-
entifically achievable, it is not economically affordable. Therefore, it
is not a common treatment of waste tires.

4 Reclaim: Reclaiming is a procedure, in which scrap tire rubber is
converted into a state using mechanical and thermal energy and
chemicals, where it can be mixed, processed, and vulcanized again.

5 Ground rubber applications:
6 Asphalt rubber is the largest single market for ground rubber.
Blending ground tire rubber with asphalt can improve some features
of highway asphalt such as longer lasting road surfaces, reduced
road maintenance, lower road noise, shorter breaking distances, etc.

7 Athletic and recreational applications including ground cover under
playground equipment and running track material

8 Agricultural and horticultural applications as well as soil betterment
9 Molded rubber products, e.g., carpet underlay, dock bumpers, roof
walkway pads, rubber tiles and bricks, etc.

Regarding the above explanations, the old negative attitude towards
used tires as an environmental hazard costing problem can be changed
into a positive perspective with an economic chance and a great op-
portunity. In other words, by holistic and accurate planning in this
field, not only the environmental issues of used tires can be resolved,

but also great financial gains could be achieved.
Today, millions of tires are used each year and with the growing

concern about environmental issues in recent years, the problem of
used tires disposal has attracted many practitioners and researchers.
Wide-ranging research efforts are made to reduce the impact of used
tires on the environment; however, applicable operational research
articles on management systems of used tires are still scarce and the
literature provides only a few mathematical models as explained in
Section 2. Moreover, to the best of the authors’ knowledge, no pub-
lished article has addressed the integration of the SCM and CRM in the
operational research models of the problem.

The remainder of this paper is organized as follows. In Section 3, the
problem is defined in detail and a novel scenario-based tri-objective
mathematical model is presented for the proposed problem and its as-
sumptions. For the solution procedure, a two-stage solution approach
including robust optimization and revised multi-choice goal program-
ming is explained in Section 4. The proposed model is validated by an
industrial case study, which is represented in Section 5. Finally, in
Section 6, conclusions and further research guidelines are given.

2. Literature review

2.1. Previous researches in product recovery considering uncertainty

Product recovery is the use of end-of-life products with the aim of
reducing waste and thereby increasing profit. In supply chain networ

k

design, product recovery can be applied to reverse logistics, a closed-
loop supply chain (CLSC) and also forward/reverse SC, depending on
the nature of the product.

For example, in reverse logistics, Realf et al. (2004) discussed the
strategic design of a reverse production system for carpet recycling
industry in the United States. They presented a mixed integer linear
programming (MILP) model to maximize the net profit. They used ro-
bust optimization to consider the uncertainty in their model. Kara and
Onut (2010) addressed the paper recycling reverse supply chain net-
work design and proposed a mixed integer revenue-maximization
model following a two-stage stochastic and robust optimization ap-
proach. Ayvaz et al. (2015) presented a two-stage stochastic program-
ming model to maximize the profit from electrical and electronic
equipment waste recycling companies. Yu and Solvang (2017) proposed
a single-objective stochastic programming model with carbon emission
constraint for sustainable reverse logistics design. Rahimi and
Ghezavati (2018) developed multi-objective MILP for recycling con-
struction and demolition waste reverse logistics network design using
two-stage stochastic programming. In their model, the objectives were
to maximize profit and social impact and minimize environmental ef-
fects.

In the CLSC, Mohamadpour Tosarkani and Hassanzadeh Amin
(2018) introduced a multi-objective model considering green factors for
battery CLSC using fully fuzzy programming. Paydar et al. (2017) de-
signed a CLSC network for used engine oil and considered two objective
functions of maximizing profit and minimizing risk. In order to deal
with uncertainty, they used robust optimization techniques.

As mentioned before, depending on the nature of products, their
recovery can be done in forward and reverse directions in a supply
chain. In this area, El-Sayed et al. (2010) developed a stochastic MILP
model for forward-reverse logistics network design under risk with the
objective of maximizing expected profit. Hatefi and Jolai (2014) con-
sidered both uncertain parameters and facility disruptions in their
forward-reverse logistics network design. The objective function of the
model was to minimize the nominal cost, and robust optimization was
utilized to consider the uncertainty in the network. Mirmajlesi and
Shafaei (2016) investigated short-lifetime products and presented ro-
bust MILP for the forward-reverse supply chain.

This brief review of the researches on product recovery systems was
to clarify the fact that depending on the product and/or industry being

M. Yadollahinia et al. Resources, Conservation & Recycling 138 (2018) 215–228

216

considered in the study, the structure of the SC can be different. In this
paper, a tire forward and reverse supply chain design is investigated, as
explained in detail in Section 3.

2.2. Previous researches in used tires

In the used tire industry, there are few articles following the
mathematical modeling approach in designing SCs. To the best of the
authors’ knowledge, the newest and most famous researches in this field
are as follows:

Dehghanian and Mansour (2009) designed a sustainable recovery
network for the waste tire industry using life cycle analysis to assess the
environmental impacts of various end-of-life alternatives. They be-
lieved that retreading plants work below their capacities because cus-
tomers find retreaded tires uneconomical. They selected grinding
rubber and incineration in cement kiln as more appropriate approaches
to deal with used tires. In the mentioned paper, a deterministic model
was presented considering only the reverse direction of the supply
chain. Subulan et al. (2015) proposed a deterministic MILP model for
the tire closed-loop supply chain. They considered environmental issues
by means of a life cycle assessment and Eco-indicator 99. To deal with
scrap tires, they suggested retreading, recycling, landfilling and using as
fuel. Finally, it was highly recommended to consider uncertainties of
various parameters in tire supply chain modeling in future researches.

Pedram et al. (2017) proposed a single objective, single period MILP
model considering the uncertainty of some parameters by the scenario
analysis for the tire CLSC. In their model, it was assumed that used tires
with minimum quality level for remanufacturing are sent for retreading
and the rest are shipped to recycling centers. Their suggestion for fur-
ther research was to work on multi-objective models of the problem.
Amin et al. (2017) developed a single objective MILP model for the tire
CLSC considering uncertainty for a case in Toronto, Canada, with the
aim of maximizing the total profit of the network. In their simple supply
chain network, only a general option of recycling used tires was con-
sidered and various options for scrap tires were not studied.

A critical analysis of the literature calls for scrutinizing the various
used tires handling methods mentioned in the previous section. The
most controversial method on which the most number of articles are
published is retreading. For many years, retreading has been considered
as an efficient way to solve the problem of used tires regarding its en-
vironmental impact and economical aspect. Since users mostly have
doubt about safety of retreaded tires and the expectation of consumers
preferring brand new tires rather than second-hand ones has increased,
retreading slowly fell out of favor giving space for introduction of a
more appealing solution.

Studies show that grinded rubber can be used as an efficient raw
material in many industries as clarified in Section 1. Adding ground
rubber to the ingredients of some special products, e.g. asphalt, im-
proves many of their features. Farina et al. (2017) showed that asphalt
pavements containing crumb rubber are better than their more common
counterparts in terms of life cycle. Furthermore, this approach is very
cost-effective and great profit could be made using appropriate and
comprehensive planning. The main purpose of this study is to provide
such extensive planning for real world problems of this kind.

Obviously, if the grinding of used tires and their application as ad-
ditives is to be considered, the structure of the supply chain could not be
the CLSC anymore. This is one of the main differentiating features of this
study from those previously published considering retreading. More spe-
cifically, the CLSC structure is replaced by forward and reverse SC con-
figuration. In addition, the uncertainty of parameters, which is their in-
herent feature in the real world, should be considered in applied studies. In
this study, a multi-objective, multi-period, multi-product MILP model
under uncertain demands and capacities is proposed, and the CRM concept
is innovatively incorporated into the SCM structure. This is one of novel-
ties and advantages of this study over the previous studies. In addition, no
previous research is reported on integrating the CRM and SCM.

3. Problem explanation

3.1. Problem definition

The structure of the investigated forward SC, showing the manu-
facturing plant, distribution centers and customers, is illustrated in
Fig. 1. The manufacturing plant produces new products, which are
delivered to customers via distribution centers. This is a schematic re-
presentation of a supply chain in the tire industry operating in Iran.
Obviously, the treatment of used tires is totally neglected in this SC.

In order to improve the existing forward SC in terms of used tire
waste management, a design for the reverse network for used products
with a focus on collecting and recycling considering the coordination in
the whole SC is developed as shown in Fig. 2. The proposed open-loop
(forward/reverse) SC network in this study is a multi-echelon network
including a manufacturing plant, recycling plants, distribution, collec-
tion and hybrid centers and three types of customers.

In the proposed network configuration, in the forward path, new
products are conveyed from the existing manufacturing plant to type

1

Fig. 1. Structure of the existing forward SC.

Fig. 2. Structure of the proposed SC network.

M. Yadollahinia et al. Resources, Conservation & Recycling 138 (2018) 215–228

217

customers via the existing distribution centers. In the backward path,
the returned (used) products from Types 1 and 2 customers are trans-
ported to collection centers and hybrid centers. Finally, the used pro-
ducts are shipped to recycling plants, from which the recycled products
are delivered to Type 3 customers.

The key assumptions of this research are as follows:

• Since one of the main strategies of the CRM is to value key custo-
mers, customer segmentation, as of the fundamental essences of the
CRM, is utilized in this study to categorize customers into three
types of customers. Specifically, Type 1 customers are the ones who
buy our new products in the forward flow and return their used
products to the SC in the reverse flow. It is worth mentioning that
customers of this type are the most valuable SC customers and are
considered as key customers. Type 2 customers are those in the
reverse flow of the SC buying the used products. Finally, Type

3

customers purchase the recycled products.

• In order to incorporate the reverse flow into the existing forward SC,
collection centers should be decided on carefully. In this paper,
three options are considered. Option 1 is to enable the existing
distribution centers to also work as collection centers. In other
words, distribution centers can be changed into hybrid (collection/
distribution) ones. Option 2 is to open some new collection centers
and Option 3 is to establish hybrid centers capable of acting as
collection and distribution centers simultaneously.

• The model is a case-based logistics network. It is basically designed
for the new and emerging industry of recycling used tires in Iran.
However, without loss of generality, through minor modifications,
the proposed model can be applied to many other industries, such as
digital and electronic equipment industries.

• Demand in both forward and reverse directions, i.e. demand for new
and recycled products, is assumed to be subject to uncertainty.
Moreover, it is assumed that the capacity of manufacturing and
recycling plants can be expanded to some extent through optimi-
zation and soft improvement attempts such as working time effi-
ciency enhancement. Uncertain parameters in this paper are ex-
plained in terms of scenarios.

• Because of the nature of the product, i.e. tires, the structure of the
proposed SC network is open loop. In other words, forward and
reverse flows are considered without being connected and/or
closed. The recycled tire is not necessarily used in the tire industry.
Specifically, the recycled tire rubber can be used in tire derived fuel,
construction industry, molded rubber products, agriculture industry,
and recreational and sports applications as well as in rubber mod-
ified asphalt applications (Presti, 2013).

• There are multiple products and multiple periods.
• The location of customers, the manufacturing plant and distribution
centers is fixed and predefined.

• The potential location of recycling plants, collection centers and
hybrid centers is known.

• For distribution, collection and hybrid centers, a minimum accep-
table utilized capacity and a maximum capacity in both forward and
reverse directions are considered due to the type of facilities, as
described in detail in Section 3.2.3.

• As mentioned above, in this paper, the CRM concept is incorporated
into the SCM decision-making process. Therefore, some CRM op-
tions and also electronic CRM (ECRM) options are defined and
considered as binary variables in model formulation. The details of
the CRM options are as follows:

• This is an option, in which the SC would give a new product for free
to customers in exchange of β units of the used product being
brought to SC collection centers by customers.

• It is an option that focuses on key customers (Type 1 customers

)

exclusively. Here, guarantee is considered only for key customers in
order to differentiate between customers with the aim of enhancing
the value of key customers as well as motivating customers of other

categories to be much more loyal to the SC.

• In this option, vehicles are sent to customers who return more than
10 units of the used product to the SC.

• This is an option to propagate the culture of returning and conse-
quently recycling used products by means of advertising.

The ECRM options are:

A It is an option to enhance customers’ willingness to be in contact
with the SC by facilitating the means of telecommunication, i.e.
establishing channels such as SMS, internet and interactive voice
response (IVR) systems with the following goals: 1) explaining the
significance of recycling for this specific kind of used product as well
as its reasons and necessity in order to increase customer awareness
level, which, in turn, results in an increase in customer cooperation
and involvement; 2) Answering customers’ possible questions and
clarifying their doubts and ambiguities, e.g. about the location of
the nearest collection or hybrid center to a customer’s place to re-
turn used products.

B This is an option, in which the SC would inform the customers in the
forward direction about the useful lifecycle of their products and
reminds them when products reach their end-of-life period and
should be returned to the SC for recycling.

With the above assumptions in mind, the main issues to be ad-
dressed by this study are to choose the location and determine the
number of collection, hybrid centers and recycling plants as well as to
determine the existing distribution centers that need to be changed into
hybrid ones as well as the quantity of products transported between
each pair of network facilities along each capacity-constrained stage
under uncertainty of parameters. Moreover, production and recycling
quantity, inventory and backorder levels at each period and CRM de-
cision variables are determined to optimize the objectives described in
what follows.

The proposed model is to optimize three objective functions. The
first objective is to maximize the total profit of the chain, the second
one seeks to maximize the total collected used products from customers
by means of increasing customer satisfaction and CRM strategies, and
the third objective is to minimize the total distance travelled between
the collecting facilities that are meant to be opened and the location of
customers who return their used products the most. In other words, this
objective is to locate and open the collecting facilities that are closer to
customers with the highest cooperation with the reverse chain. It is
worth mentioning that the second and third objectives, which in-
corporate the CRM concept in this study, search in line with each other
and the reason for their separate formulation is that they are inherently
different.

3.2. Problem formulation

To describe the aforementioned SC network, the following notations
are used in the model formulation:

3.2.1. Notations
Indices:

l Index for fixed locations of manufacturing plants, (l = 1,2,…,L)
i Index for fixed locations of distribution centers, (i = 1,2,…,I)
j Index for potential locations for collection centers, (j = 1,2,…,J)
k Index for potential locations for hybrid centers, (k = 1,2,…,K)
m Index for fixed locations of Types1 and 2 customers,

(m = 1,2,…,M)
n Index for potential locations available for recycling plants,

(n = 1,2,…,N)
o Index for fixed locations of Type 3 customers, (i = 1,2,…,O)

M. Yadollahinia et al. Resources, Conservation & Recycling 138 (2018) 215–228

218

p Index for products in the forward direction, (i = 1,2,…,P)
p′ Index for recycled products in the reverse direction,

(p′ = 1,2,…,P′)
t Index for periods, (t = 1,2,…,T)
a Index for CRM options
s Index for scenarios, (s = 1,2,…,S)

Parameters:

PPpt Unit price of product p in period t
PAp′t Unit price of recycled product p′ in ton in

period t
PCpt Unit supply and production cost of product

p in period t
RCp′t Unit supply and production cost of

recycled product p′ in ton in period t
Dmpts Demand of customer m for product p in

period t under scenario s
D′op′ts Demand of customer o for recycled

product p′ in ton in period t under scenario
s

TDpt Unit transportation cost for product p
shipped from manufacturing plant to
distribution/ hybrid centers in period t

TRpt Unit transportation cost for product p
shipped from collection/hybrid centers to
recycling plants in period t

TO p′t Unit transportation cost for recycled
product p ́ shipped from recycling plants to
Type 3 customers in period t

ICi Fixed cost for changing distribution center
i into a hybrid one

FCCj Fixed cost for opening collection center j
FCHk Fixed cost for opening hybrid center k
FCn Fixed cost for opening recycling plant n
αt Quantity of used products that can be dealt

with without customer engagement, e.g.
finding and using a landfill in time period t

γ Average quantity of products being used in
the country in each period

β Number of used products that customers
should return in order to have a free new
product

DAli The distance between manufacturing plant
l and distribution center i

DBlk The distance between manufacturing plant
l and hybrid center k

DCkn The distance between hybrid center k and
recycling plant n

DDjn The distance between collection center j
and recycling plant n

DEin The distance between distribution center i
and recycling plant n

DFno The distance between recycling plant n
and customer o

MXCj Maximum capacity of collection center j
MNCj Minimum acceptable capacity utilization

of collection center j
XCFk Maximum capacity of hybrid center k in

receiving products in the forward direction
NCFk Minimum acceptable capacity of hybrid

center k in receiving products in the
forward direction

XCRk Maximum capacity of hybrid center k in
receiving used products in the backward
direction

NCRk Minimum acceptable capacity of hybrid
center k in receiving used products in the
backward direction

UFi Maximum capacity of distribution center i
in the forward direction

URi Maximum capacity of distribution center i
that changed into hybrid in the backward
direction

CAPpls Capacity of manufacturing product p in
manufacturing plant l under scenario s

CNns Capacity of recycling plant n under
scenario s

MMp′ The matrix to change the number of unit of
products into the equivalent weight of
products’ components in ton

HHplt Unit inventory holding cost of product p in
manufacturing plant l in period t

CBOpt Unit backorder cost of product p in period t
M A sufficiently large positive number
COB Cost of option B
COC Cost of option C
COD Cost of option D
COE Cost of option E
COF Cost of option F
a=[aA,aB,aC,aD,aE,aF] The influence vector representing the

impact level of the defined CRM options
on customer satisfaction

Decision variables:

Qplt Quantity of product p produced by manufacturing plant l in
period t

Q′p′nt Quantity of recycled product p′ produced by recycling plant
n in period t

ABplit Quantity of product p shipped from manufacturing plant l to
distribution center i in period t

ACplkt Quantity of product p shipped from manufacturing plant l to
hybrid center k in period t

ADpimt Quantity of product p shipped from distribution center i to
customer m in period t

AEpkmt Quantity of product p shipped from hybrid center k to
customer m in period t

AFpmkt Quantity of product p shipped from customer m to hybrid
center k in period t

AGpmjt Quantity of product p shipped from customer m to collection
center j in period t

AHpmit Quantity of product p shipped from customer m to
distribution center i in period t

AIpjnt Quantity of product p shipped from collection center j to
recycling plant n in period t

AJpint Quantity of product p shipped from distribution center i to
recycling plant n in period t

AKpknt Quantity of product p shipped from hybrid center k to
recycling plant n in period t

AL
p′n-

ot

Quantity of recycled product p′ shipped from recycling plant
n to customer o in period t

ANplts Inventory level of product p at plant l in period t under
scenario s

BOmpts Backorder level of customer m for product p in period t
under scenario s

M. Yadollahinia et al. Resources, Conservation & Recycling 138 (2018) 215–228

219

Xi 1 If distribution center i is opened, 0 otherwise
Yj 1 If collection center j is opened, 0 otherwise
Zk 1 If hybrid center k is opened, 0 otherwise
Wn 1 If recycling plant n is opened, 0 otherwise
OA 1 If option A is activated, 0 otherwise
OB 1 If option B is activated, 0 otherwise
OC 1 If option C is activated, 0 otherwise
OD 1 If option D is activated, 0 otherwise
OE 1 If option E is activated, 0 otherwise
OF 1 If option F is activated, 0 otherwise
FFpmt Total number of free product p given to customer m as

defined in CRM options in period t

3.2.2. Objective functions
The three objectives of the presented model are maximizing the

total profit, maximizing total customer satisfaction and minimizing
total distance between collecting facilities that are meant to be opened
and customers who return their used products the most.

∑ ∑

∑ ∑

∑ ∑ ∑ ∑

=

+

+

′ ′

f AD AE PP

AL PA

Max ( )s
t p i m

pimt
k m

pkmt pt

t p n o
p not p t

1

(1)

∑ ∑ ∑ ∑ ∑ ∑− −


′ ′PC Q RC Q
t p l

pt plt
t n p

p t p nt
(2)

∑ ∑ ∑ ∑ ∑ ∑

∑ ∑ ∑ ∑ ∑ ∑
∑ ∑ ∑ ∑ ∑ ∑

− +

+

− +


′ ′

TR AK DC AI DD

AJ DE TO AL DF

TD AB DA AC DB

(

)

( )

t p
pt

k n
pknt kn

j n
pjnt jn

i n
p in

t p n o
p t p not no

t p
pt

l i
plit li

l k
plkt lk

int

(3)

∑ ∑ ∑ ∑− − − −FCC Y FCH Z FC W IC X
j

j j
k

k k
n

n n
i

i i

(4)

∑ ∑ ∑ ∑ ∑ ∑− × − ×HH IN CBO

BO

t l p

plt plts
m p t

pt mpts
(5)

∑ ∑ ∑− × × − × − ×

− × − × − ×

FF PP OA COB OB COC OC

COD OD COE OE COF OF

( )

( ) ( )

( ) ( ) ( )

t p m
pmt pt

(6)

=f
γ

V U aMax
1

( ( ) )2 (10)

∑ ∑ ∑ ∑ ∑ ∑ ∑

∑ ∑ ∑ ∑
= +
+

f AF DG AG DH

AH DI

Min
t p m k

pmkt mk
t p m j

pmjt mj

t p m i
pmit mi

3

(11)

The first objective is to maximize the total profit by subtracting the
total cost from the total revenue. The terms of this objective are as
follows: (1) Total revenue of selling new products in the forward di-
rection and the recycled products in the reverse direction; (2) the total
supply and production cost in manufacturing plants and recycling cost
in recycling plants; (3) the total transportation cost; (4) the fixed cost
for establishing new facilities such as collection centers, hybrid centers,
recycling plants and the fixed cost of converting the existing distribu-
tion centers into hybrid ones; (5) the total inventory carrying cost and
backorder cost; (6) the total CRM cost. It is notable that the term

×FF PPpmt pt in (6) calculates the cost of option A.
The second objective is to maximize the number of used products

being collected from customers. In order to do that, the compound
function V(U(a)) is defined. U(a) is considered as a function for cal-
culating customer satisfaction level, and generally can be defined in
different forms. In this study, the mentioned function U(a) is introduced

as follows:

=









U a a a a a a a

OA
OB
OC
OD
OE
OF

( ) [ , , , , , ]A B C D E F

(12)

In order to increase customer satisfaction, some CRM options OA-
OF, which are predefined and described in Section 3.2.1, are fed into
the model as binary variables. These options are established on the
basis of CRM strategies based on the three reasons mentioned in Section
3.1 and with the aim of increasing customer engagement. The influence
coefficient vector, a=[aA,aB,aC,aD,aE,aF], represents the impact level of
CRM options on customer satisfaction.

The range of the function U, described above, is the domain of the
function V that calculates the quantity of the total received used pro-
ducts from customers considering customer satisfaction. Here, the
function is assumed to be linear; however, in case of non-linearity, it
can be approximated satisfyingly using linear formulation and fine
tuning through interviews, questionnaires and other data collection
tools. Finally, the compound function is divided by the average quantity
of products being used in the country in every period shown by γ. This
makes the value of the objective function normalized and fall between 0
and 1. The third objective, which can also be considered as a CRM
objective, is to minimize the distance between collecting facilities
(collection, hybrid and distribution centers that are converted into
hybrid ones) that are meant to be opened and customers who return
their used products the most.

3.2.3. Constraints
This subsection is devoted to present the constraints of the proposed

model. The constraints are categorized into different categories ex-
plained in what follows.

3.2.3.1. Balance constraints. These constraints, presented in following
equalities and inequalities, are used to ensure the balance in flow and
inventory of products throughout the entire SC.

− + = ∑ + ∑ ∀−Q IN IN AB AC p l t, ,plt plts plt s i plit k plkt1 (13)

∑ = ∑ − ∑ ∑ + ∑ ∑

+ ∑

BO D AD AE

BO

p t s( )

, ,m mpts m mpts i m pimt k m pkmt

m mpt s1

(14)

′ = × ∑ ∑ + ∑ + ∑ + ∀ ′′ ′Q MM AK AI AJ α p t n( ) , ,p nt p p k pknt j pjnt i pint t

(15)

∑ ≤ ′ ∀ ′′ ′AL D p o n s, , ,n p not op ts (16)

′ = ∑ ∑ ∀ ′′ ′Q AL p t n, ,p nt n o p not (17)

∑ = ∑ ∀AD AB p i t, ,m pimt l plit (18)

∑ = ∑ ∀AH AJ p i t, ,m pmit n pint (19)

∑ = ∑ ∀AC AE p k t, ,l plkt m pkmt (20)

∑ = ∑ ∀AF AK p k t, ,m pmkt n pknt (21)

∑ = ∑ ∀AG AI p j t, ,m pmjt n pjnt (22)

∑ ∑ + ∑ + ∑ ≤ ∀AF AG AH γ p t( ) ,m k pmkt j pmjt i pmit (23)

Constraint (13) ensures that at each period and for each product, the
flow entering each manufacturing plant and its residual inventory from
the previous period is equal to the summation of the amount

M. Yadollahinia et al. Resources, Conservation & Recycling 138 (2018) 215–228

220

transported to distribution centers and the residual inventory.
Constraint (14) ensures that in each time period and for each product
and under each scenario, the sum of the total flow to customers and
total amount of backorders is equal to the sum of the total demand and
accumulated backorders. Constraint (15) ensures that the total amount
of recycled products produced by each recycling plant is equal to the
sum of the collected used product flow entering the plant. Constraint
(16) ensures that in each period and under each scenario, the total
amount of each product transported to each customer does not exceed
the total demand of the customer for that product. Constraint (17)
ensures that the total amount of recycled products produced by each
recycling plant is equal to the total amount transported to each Type 3
customer. Constraints (18)–(21) are the balance constraints in the
forward and reverse direction of the distribution and hybrid centers,
respectively. Constraint (22) is the balance constraint of collection
centers. Constraint (23) is to restrict the sum of the collected used
products.

3.2.3.2. Capacity constraints. The following constraints are to define
and apply capacities of facilities.

≤ ∀Q CAP p t l s, , ,plt pls (24)

∑ ′ ≤ × ∀′ ′Q CN W t n s, ,p p nt ns n (25)

∑ ∑ ≤ × ∀AG MXC Y j t,p m pmjt j j (26)

∑ ∑ ≥ × ∀AG MNC Y j t,p m pmjt j j (27)

∑ ∑ ≤ × ∀AC XCF Z k t,p l plkt k k (28)

∑ ∑ ≥ × ∀AC NCF Z k t,p l plkt k k (29)

∑ ∑ ≤ × ∀AF XCR Z k t,p m pmkt k k (30)

∑ ∑ ≥ × ∀AF NCR Z k t,p m pmkt k k (31)

∑ ∑ ≤ ∀AB UF i t,p l plit i (32)

∑ ∑ ≤ ∀AH UR i t,p m pmit i (33)

Constraints (24) and (25) ensure that the total amount of products
processed in each manufacturing and recycling plant does not exceed
the corresponding capacity limit of each facility under each scenario,
respectively. Eqs. (26) and (27) are the maximum and minimum ca-
pacity constraints for opening collection centers. For opening hybrid
centers, maximum and minimum capacity constraints are considered
for both the forward and reverse directions through Constraints
(28)–(31). Since it is assumed that the existing distribution centers can
be changed into hybrid ones, only maximum capacity constraints in the
forward and reverse flows, as presented in Constraints (32) and (33),
are applicable.

3.2.3.3. Shipping-linking constraints. In order to preserve the
consistency and integrity of the model regarding the network links
and shipping, the following constraints are considered. Therefore,
Constraints (34)–(45) ensure that there is no shipping between any
non-linked locations.

≤ × ∀AC M Z p t l k, , ,plkt k (34)

≤ × ∀AE M Z p t m k, , ,pkmt k (35)

≤ × ∀AF M Z p t m k, , ,pmkt k (36)

≤ × ∀AG M Y p t m j, , ,pmjt j (37)

≤ × ∀AH M X p t m i, , ,pmit i (38)

≤ × ∀AK M W p t k n, , ,pknt n (39)

≤ × ∀AK M Z p t k n, , ,pknt k (40)

≤ × ∀AI M W p t j n, , ,pjnt n (41)

≤ × ∀AI M Y p t j n, , ,pjnt j (42)

≤ × ∀AJ M W p t i n, , ,p nint (43)

≤ × ∀AJ M X p t i n, , ,p iint (44)

≤ × ∀ ′′AL M W p t n o, , ,p not n (45)

3.2.3.4. CRM constraint. Constraint (46) calculates the number of new
products that the SC must yield for free to customers who return their
used products to the chain if option A is activated.

= ⎢
⎣⎢


⎦⎥


∑ + ∑ + ∑

FF p m t, ,pmt
AF AG AH

β
k pmkt j pmjt i pmit

(46)

3.2.3.5. Logical constraints. Constraints (47) and (48) impose the binary
and non-negativity restriction on the corresponding decision variables.

∈ ∀X Y Z W OA OB OC OD OE OF o i j k n, , , , , , , , , { , 1} , , ,i j k n (47)


∀ ′

Q Q AB AC AD AE AF AG
AH AI AJ AK AL IN BO FF

p p t i j k l m n o s
, , , , , , , ,

, , , , , , , 0
, , , , , , , , , ,

plt p nt plit plkt pimt pkmt pmkt pmjt

pmit pjnt p pknt p not pts mpts pmtint

(48)

3.2.4. Linearization
Clearly, the first objective function is non-linear in term (6). To

linearize this term, a non-negative auxiliary variable is introduced as
= ×FOA FF OApmt pmt and the following constraints are added to the

original model.

≥ − − ∀FOA FF M OA p m t(1 ) , ,pmt pmt (49)

≤ + − ∀FOA FF M OA p m t(1 ) , ,pmt pmt (50)

≤ × ∀FOA M OA p m t, ,pmt (51)

≥ ∀FOA p m t0,integer , ,pmt (52)

Proof: Two states are imaginable for FOApmt:
(i) If OA = 0, then FOApmt = 0. In this case, we have

≥ −FOA FF Mpmt pmt

≤ +FOA FF Mpmt pmt

≤ ×FOA M 0.pmt

Since FFpmt is a positive integer variable, it clearly takes zero.
(ii) If OA = 1, then FOApmt=FFpmt. In this case, we have

≥FOA FFpmt pmt

≤FOA FFpmt pmt

≤ ×FOA M 1.pmt

In this situation, it is clear that FOApmt=FFpmt.
Obviously with the definition of the auxiliary variable, the non-

linear part of the first objective function is changed to:

∑ ∑ ∑− × − × − ×

− × − × − ×

FOA PP COB OB COC OC

COD OD COE OE COF OF
( ) ( ) ( )
( ) ( ) ( )
t p m
pmt pt

(53)

M. Yadollahinia et al. Resources, Conservation & Recycling 138 (2018) 215–228

221

4. Solution procedure

To solve the proposed scenario-based model presented in Section
3.2, a two-phase procedure is utilized. The model is adapted to an
equivalent robust counterpart in the first phase. A revised multi-choice
goal programming method (RMCGP) is used for obtaining an efficient
solution in the second phase.

4.1. Step1: robust optimization

In this paper, the robust optimization is applied to deal with un-
certainty because of the advantages of the robust approach. More spe-
cifically, the SC design not only has to be aligned with the expected
conditions, but also has to be robust and flexible enough to adjust to the
inherent changes emerging in uncertain future realizations. Robust
solutions allow for flexibility to enable stable performance of the SC
under uncertain conditions. The robust optimization is an operation
research framework that deals with uncertainties in generic optimiza-
tion problems and finds robust solutions to these problems (Paixao and
Souza, 2015). More specifically, it is possible to encompass service-level
or decision-makers’ (DMs’) risk aversion function by means of the ro-
bust optimization approach, which was first introduced by Mulvey et al.
(1995) and also to attain a series of solutions that are progressively less
sensitive to the realizations of the data in a scenario set. For the men-
tioned reasons, robust optimization is used in this study rather than a
more used approach called stochastic approach. In stochastic linear
programming, there is no use of penalty terms and stabilizing the so-
lution over a period of time is not possible; it just simply minimizes the
expected cost or maximizes the expected profit.

The optimal solution acquired by a robust optimization model is
called robust if the solution remains ‘close’ to optimal after changing
the input data. This is known as solution robustness. Furthermore, the
model robustness becomes important when a solution is ‘almost’ fea-
sible for small changes in the input data. The robust optimization model
structure is as follows:

Min cTx + dTx

s.t.

Ax = b

i + Cy = i

x,y ≥ 0

where the coefficient A is the matrix of certain parameters and x is a
vector of design variables. Moreover, coefficients B and C are the ma-
trices of noisy parameters and y is the vector of control variables.

In a scenario-based approach, we have a set of scenarios Ω=[1,2,
…,S], in which each scenario is associated with a set of control con-
straints [ds, Bs, Cs, es] and a probability of occurrence ps where ob-
viously Σ Ps = 1. Based on the above standard problem, the robust
optimization problem can be formulated as below:

Min σ(x,y1,y2,y1,…,ys) + ωp(δ1,δ2,…,δs)

s.t.

Ax = b

+ + = ∀B x C v δ e ss

s s s

≥ ∀x y s, 0

The second term of the above objective function preserves the
model robustness and considers the fact that with a set of input para-
meters under some scenarios, infeasible results may be obtained. In the
above problem, ω is the infeasibility weight of a scenario. For the first
term of the objective function mentioned above, Leung et al. (2002)
presented a much more applicable formulation as follows:

∑ × + × ∑ × − ∑ ′ +

− ∑ × + ≥

= = ′= ′

′= ′ ′

p ξ λ p ξ p ξ θ
s t
ξ p ξ θ
θ

Min [( ) 2 ]
. .

0
0

s
S

s

s s
S

s s s
S
s s s
s s
S
s s s
s

1 1 1

1

where ξs is the minimization objective function in the original optimi-
zation problem. For more detailed information, the works of Mulvey
and Ruszczyński (1995); Mulvey et al. (1995) and Leung et al. (2002)
are recommended.

By focusing on robust optimization, the robust counterpart of the
proposed scenario-based model presented in Section 3.2 can be for-
mulated. In this model, the parameters Dmpts, Dʹoptʹs, CAPpls, and CNns
vary in a given uncertainty set S. The following parameters should also
be added to the presented model.

ps Probability of occurrence scenario s
W1 Penalty of one unit under-fulfillment of demand of product p in

the forward direction
W2 Penalty of one unit under-fulfillment of demand of recycled

product p′ in the backward direction
W3 Penalty of leakage capacity for producing of product p
W4 Penalty of leakage capacity for producing of recycled product p′

The control variables of the model are as follows:

δ1mpts The under-fulfillment of demand of product p for customer m
in period t under scenario s

δ2op′ts The under-fulfillment of demand of product p for customer o
in period t under scenario s

δ3pls The under-fulfillment of capacity for producing product p at
plant l under scenario s

δ4ns The under-fulfillment of capacity for producing at recycling
plant n under scenario s

Due to the uncertain parameters explained above, only the first
objective function, which is maximizing the total profit, is subject to
uncertainty. Therefore, in the robust counterpart of the model, the
second and third objective functions (10) and (11) are considered with
no changes. However, the first objective is as follows:

∑ ∑ ∑
∑ ∑ ∑ ∑
∑ ∑ ∑ ∑ ∑ ∑ ∑
∑ ∑

′ = − × + ⎡


⎢ − × +


+ × ×

+ × × + × ×

+ × ×

f P f λ P f P f θ

W P δ

W P δ W P δ

W P δ

Min ( )

2

1

2 3

4

s
s s

s
s s

s
s s s

m p t s
s mpts

o p t s
s op ts

p l s
s pls

n s
s ns

1 1 1 1

1
2 3

4
(54)

Since our first original objective function f1s is to be maximized, in
order to replace that as ξs, which is to be minimized as mentioned
above, the term ∑ ×p fs s s1 is written with a negative sign in (54).

∑ = ∑ − ∑ ∑ + ∑ ∑

+

∑ +



BO D AD AE

BO δ

p t s( )

1
, ,m mpts m mpts i m pimt k m pkmt

m mpt s mpts1

(55)

∑ ≤ ′ + ∀ ′′ ′ ′AL D δ p o n s2 , , ,n p not op ts op ts (56)

≤ + ∀Q CAP δ p t l s3 , , ,plt pls pls (57)

∑ ′ ≤ + × ∀′ ′Q CN δ W t n s( 4 ) , ,p p nt ns ns n (58)

M. Yadollahinia et al. Resources, Conservation & Recycling 138 (2018) 215–228

222

− ∑ × + ≥ ∀f P f θ s0s s s s s1 1 (59)

≥ ∀′θ δ δ δ δ p p t l m n s, 1 , 2 , 3 , 4 0 , , , , , ,s mpts op ts pls ns (60)

All the constraints (13)–(52) described for the presented scenario-
based model in Section 3.2 are considered in the robust counterpart in
the same form, except for (14), (16), (24), and (25) that are changed
into (55)–(58), respectively. Constraint (59) is for the linearization of
the objective function, which is defined in the work of Leung et al.
(2002). Finally, Equation (60) imposes the variables as positive real
numbers.

4.2. Step2: multi-objective methodology: RMCGP

The goal programming (GP) is a vital technique for decision-makers
to solve multi-objective decision-making (MODM) problems in
achieving a set of satisfying solutions. Charnes and Cooper (1957) first
introduced the GP; a standard model of GP can be shown as follows:

∑ +

− + = =

= ≤

≥ =

≥ =

=

+ −

+ −
+ −

ω d d
s t
f X d d b i n

h X k q
d d i n

Min ( )
. .
( ) 1, 2, …,

( ) ( or ) 0 1, 2, …,
, 0 1, 2, …,

i
n

i i i

i i i i

k
i i
1

where
fi(X): goal constraint i
hk(X): system constraint k
ωi: the weight of the ith goal
bi: the aspiration level of goal i
d+i and d


i : positive and negative deviations from the target value of

goal i, respectively, which

= ⎧
⎨⎩

− <

= ⎧
⎨⎩

− >


+

d
b f X f X b

d
f X b f X b

( ) if ( )
0 otherwise

( ) if ( )
0 otherwise

i
i i i i

i
i i i i

The standard GP technique emphasizes obtaining a solution next to
the aspiration level for every single objective function and imposes a
penalty on the deviation away from the aspiration level. However, in
practice, the DM usually selects a conservative initial aspiration level
based on the limited resource and available information. Hence, Chang
(2007) proposed a new method of the MCGP for the MODM with
multiple aspiration levels, which allows the DM to set multi-choice
aspiration levels for objective functions. Afterwards, Chang (2008) ex-
tended the MCGP to the RMCGP as the following two cases.

The first case: ‘The less the better’ is expressed as:

∑ + + +

= ≤ ≥ =
− + = =

− + = =
≤ ≤ =

≥ =

=

+ − + −

+ −
+ −
+ − + −

ω d d ρ

e e

s t
h X k q
f X d d R i n

R e e g i n
g R g i n

d d e e i n

Min [ ( ) ( ) ]
. .

( ) ( or ) 0 1, 2, …,
( ) 1, 2, …,

1, 2, …,
1, 2, …,

, , , 0 1, 2, …,

i
n

i i i i i i

k
i i i i
i i i i
i i i
i i i i
1

.min

.min

.max

The second case: ‘The more the better’ is expressed as:

∑ + + +
= ≤ ≥ =
− + = =
− + = =
≤ ≤ =
≥ =

=
+ − + −

+ −
+ −
+ − + −
ω d d ρ e e
s t
h X k q
f X d d R i n
R e e g i n
g R g i n
d d e e i n
Min [ ( ) ( ) ]
. .

( ) ( or ) 1, 2, …,
( ) 1, 2, …,

1, 2, …,
1, 2, …,
, , , 0 1, 2, …,
i
n
i i i i i i
k
i i i i
i i i i
i i i
i i i i
1
.max

.min .max

where
gi.max: the upper bound of the ith aspiration level
gi.min: the lower bound of the ith aspiration level
Ri: the continuous variable with a range of gi.min≤ Ri≤ gi.max,
d+i and d


i are positive and negative deviations from −f X R| ( ) |i i

ωi: the weight of the ith goal
For the first case:
e+i and e


i : positive and negative deviations from −R g| |i i.max

ρi: the weight of the sum of deviations of −R g| |i i.min
For the second case:
e+i and e


i : positive and negative deviations from −R g| |i i i.

ρi : the weight of the sum of deviations of −R g| |i i.max
According to the multi-objective methodology mentioned above, the

objective function of the RMCGP form of the proposed model is as
follows:

⎜ ⎟ ⎜ ⎟

⎜ ⎟ ⎜ ⎟
⎜ ⎟ ⎜ ⎟

= ⎛
⎝ −


+

+ ⎛
⎝ −



+
+ ⎛
⎝ −

+ + ⎛
⎝ −



+
+ ⎛
⎝ −


+ + ⎛
⎝ −


+
+ − + −
+ − + −
+ − + −

f
ω

g g
d d

ρ
g g

e e

ω
g g

d d
ρ

g g
e e

ω
g g
d d
ρ
g g
e e

Min ( ) ( )

( ) ( )
( ) ( )

4
1

1. max 1. min
1 1

1
1. max 1. min
1 1
2

2. max 2. min
2 2

2
2. max 2. min
2 2
3

3. max 3. min
3 3

3
3. max 3. min
3 3

(61)

Including the constraints of the robust counterpart, the following
new constraints emerge in the RMCGP form as explained earlier in this
section:

′ − + =+ −f d d R1 1 1 1 (62)

− + =+ −R e e g1 1 1 1. min (63)

≤ ≤g R g1. min 1 1. max (64)

− + =+ −f d d R2 2 2 2 (65)

− + =+ −R e e g2 2 2 2. max (66)

≤ ≤g R g2. min 2 2. max (67)

− + =+ −f d d R3 3 3 3 (68)

− + =+ −R e e g3 3 3 3. min (69)

≤ ≤g R g3. min 3 3. max (70)

≥+ − + − + − + − + − + −d d e e d d e e d d e e, , , , , , , , , , , 01 1 1 1 2 2 2 2 3 3 3 3 (71)

Six sub-problems with individual objective function can be solved to
find gi.max and gi.min, in which:

g1.min can be found by Min f′1
g1.max can be found by Max f′1
g2.min can be found by Min f2
g2.max can be found by Max f2
g3.min can be found by Min f3
g3.i can be found by Max f3
The DM is provided by the solutions to these sub-problems.

Consulting with experts, she/he makes decisions about the parameters.

M. Yadollahinia et al. Resources, Conservation & Recycling 138 (2018) 215–228

223

5. Case study

As mentioned in the previous sections, the proposed model is based
on a real-world problem and attempts to improve an existing forward
SC in the tire industry, design the reverse structure of the SC with the
aim of collecting and recycling waste tires, and coordinate the whole SC
under uncertainty while considering the CRM concept.

5.1. Description and input data

The existing forward tire SC network considered in the case study is
illustrated in Fig. 3. A manufacturing plant in Tehran and two dis-
tribution centers, one in Tehran and one in Isfahan, are operating. Eight
cities across the country are considered as main customer-located areas.

According to a report by the Iran Ministry of Mining and Industry,
the number of people per car is about 9 while the number of people per
scrap is about 6 in Iran (Dehghanian and Mansour, 2009). In other
words, 3.6 kg of scrap tire is produced per capita per year. This amount
is equivalent to 15 million tires, which translates to 60 thousands tons
of scrap based on the current population of the country. This amount is
expected to grow 0.02% annually. Currently, less than one third of the
produced waste tires in Iran are being recycled. This shows that tire
recycling industry is a new and emerging industry in this country.

In the described supply chain network, potential candidates for fa-
cility locations are chosen from populated cities because they are the
main points of scrap tire production. For collection centers, Tehran and
Isfahan, for hybrid centers, Isfahan and Mashhad, and for recycling
plans, Tehran and Isfahan are selected. Eight main nodes for forward
direction customers and three main nodes for backward direction cus-
tomers are considered. Two kinds of products, in each forward/back-
ward flow, are considered; in the forward direction, car and truck tires
and in the backward direction, crumb rubber and steel flow. Processing

1 ton of scrap tires gives 0.6 tons of crumb rubber (Dehghanian and
Mansour, 2009). Considering the approximate weight of a tire equal to
4 kg and using the aforementioned matrix MMp′, it is possible to convert
the forward flow, which is expressed in numbers, to the backward flow,
which is expressed in tons.

́ = =MM 0.004 [0.6 0.4] [0.0024 0.0016]p

In our case, a planning horizon of two time periods, each re-
presenting one year, is considered. Fixed opening costs for the facilities
are depicted in Table 1. The dynamic prices and costs of the chain are
illustrated in Table 2. The weight of the goals and costs of the CRM
options are displayed in Table 3. As mentioned before, the cost of op-
tion A is not predefined and is calculated in the model by COA =
FFpmt× PPpt.

Three scenarios are defined and indexed as 1, 2 and 3, which re-
present pessimistic, moderate, and optimistic situations, respectively.
Each of the scenarios represents a different situation reflecting varia-
tions in demand and capacities of the plants. It is assumed that by some
low cost efforts such as eliminating idle times of facilities, the capacity
of the manufacturing and recycling plants can be expanded to some
extent. Scenario number 3 represents the optimistic scenario because
under this scenario, capacities of the demand and plants are at max-
imum, which translate to higher earnings for the chain. The demand in

Fig. 3. The existing SC network.

Table 1
Fixed Opening Costs for the Facilities (in 10,000 Rials).

Facility type Place 1 Place 2

Distribution centers 50,000 30,000
Collection centers 7000 6000
Hybrid centers 80000 85000
Recycling plants 350000 320000

M. Yadollahinia et al. Resources, Conservation & Recycling 138 (2018) 215–228

224

the forward and backward directions and uncertainty in the capacity of
the manufacturing and recycling plants are shown in Tables 4–6, re-
spectively. Table 7 shows the maximum and minimum capacity of the
distribution, collection and hybrid centers.

5.2. Computational results

The proposed model is implemented by Lingo 9 software package
and solved by the branch-and-bound method. The main results are
presented below. The optimal SC network obtained by the model is il-
lustrated in Fig. 4, showing that in the optimal solution, a collection
center in Tehran, a hybrid center in Mashhad and two recycling plants
in the two candidate locations, i.e. Tehran and Isfahan, are opened.
Furthermore, the optimal solution indicates that it is profitable to alter
the existing distribution centers into hybrid ones rather than opening
new collection or hybrid centers because in the optimal solution, both
the distribution centers are transformed into hybrid ones.

The DM is provided by the solutions of the sub-problems gi.min and
gi.max, as explained in Section 4.2. These results are shown in Table 8
and the deviations are listed in Table 9. The quantity of the profit ob-
jective function over each scenario (f1s), expected profit, quantity of the
objective functions in the robust counterpart model, i.e. f 1׳ ,f2,f3, and
optimal minimization objective function in the goal programming
model (f4) are reported in Table 10.

From the results, it can be implied that the first objective is fully

satisfied since the deviations from the first goal (d+1 , d

1 ) are zero. Due

to the tradeoff between profit and customer satisfaction, the model
decided to activate all the CRM options defined in Section 3, except for
option A as shown in Table 11. Moreover, the optimal quantity of
products manufactured in the manufacturing plant and the recycled
products produced in recycling plants in each time period are presented
in Table 12.

5.3. Sensitivity analysis

In this section, sensitivity analyses on important parameters of the
proposed model are conducted in order to provide managerial insights.
First, β, the number of used products that should be provided by cus-
tomers so that they can receive a free new product in one of the CRM
options (A). In the computational results in Section 5.2, with β = 16,
the model decided not to activate Option A. In order to find the value of
β for which the model decided to activate Option A and the

Table 2
The Prices and Costs of the Chain (in 10,000 Rials).

t1 t2

Parameter p1 P2 P1 P2

PCpt 90 200 100 250
RCp′t 400 400 500 500
HHplt 10 11 12 13
CBOpt 50 70 60 80
TDpt 0.010 0.015 0.015 0.020
TRpt 0.005 0.010 0.010 0.015
TOp′t 1 1.5 1.5 2
PPpt 90 200 100 250
PAp′t 400 400 500 500

Table 3
Parameter Data Setting (in 10,000 Rials).

Parameter Value parameter value

COB 1000 ω1 50
COC 300 ω2 400
COD 150 ω3 50
COE 170 ω4 500
COF 1200

Table 4
Demand in the Forward Direction (in thousands).

t1 t2

s1 s2 s3

s1 s2 s3

Customer p1 p2 p1 p2 p1 p2 p1 p2 p1 p2 p1 p2

m1 1000 200 1100 250 1200 300 1020 204 1122 255 1224 306
m2 300 100 350 150 400 200 306 102 357 153 408 204
m3 300 100 350 150 400 200 306 102 357 153 408 204
m4 200 80 250 90 300 100 204 95 255 91.8 306 102
m5 300 100 350 150 400 200 306 102 357 153 408 204
m6 200 100 250 150 300 200 204 102 255 153 306 204
m7 200 150 250 200 300 250 204 155 255 204 306 255
m8 200 80 250 90 300 100 204 95 255 91.8 306 102

Table 5
Demand in the Backward Direction (in thousands).

t1 t2
s1 s2 s3 s1 s2 s3

Customer type3 p′1 p′2 p′1 p′2 p′1 p′2 p′1 p′2 p′1 p′2 p′1 p′2

o1 9 5 10 6 11 7 9.1 5.1 10.1 6.1 11.1 7.1
o2 5 3 6 4 7 5 5.1 3.1 6.1 4.1 7.1 5.1
o3 3 2 4 3 5 4 3.1 2.1 4.1 3.1 5.1 4.1

Table 6
Uncertainty in Capacity (in thousands).

s1 s2 s3

Parameter p1 p2 p1 p2 p1 p2

CAPpls 3500 1000 3600 1100 3700 1200
CNns 19 15 20 16 21 17

Table 7
Capacity of Distribution, Collection and Hybrid Centers (in thousands).

Parameter Place 1 Place 2

MXCj 5000 4000
MNCj 3000 2000
XCFk 1000 900
NCFk 800 700
XCRk 6000 4000
NCRk 4000 3000
UFi 3000 3000
URi 3000 3000

M. Yadollahinia et al. Resources, Conservation & Recycling 138 (2018) 215–228

225

consequences of activating this option, the model was run with various
values of β. Finally, it was observed that the smallest value for this
parameter for which the model decided to activate Option A was 20. At
β = 20, the amount of total profit increased significantly to 275945200,
showing an approximate 12.5% growth. Besides that, there was a great
change in the customer satisfaction objective function raising from
0.05333 to 0.06667, which showed an approximate 25% growth. It is
obvious that based on this analysis, DMs or senior managers of the SC
are able to assign a more appropriate value for the mentioned para-
meter.

As described in Section 4.2, in the MCGP model, ωi is a parameter
that shows the weight of positive and negative deviations from the goal
value of objective function i. These parameters are designated by DMs
and directly affect the optimal solution. Therefore, DMs or senior
managers of the SC should assign appropriate parameters as coefficients
for every single objective function cautiously. That is why in this paper,
the effect of different weights of each objective function on the mini-
mization objective function in the goal programming model (f4) is in-
spected in order to help DMs in assigning these weights in a way to

Fig. 4. The optimal SC network.

Table 8
The Upper/Lower Bound of the Aspiration Levels.

=g1. min 0.2452637E+10 = +g1. max 0.3188428E 10
=g2. min 0.03 =g2. max 0.07
=g3. min 8500 = +g3. max 0.2E 9

Table 9
Deviations.

=+d 01 =
−d 01 =+e 100961 =

−e 01
=+d 02 =

−d 0.01662 =+e 02 =
−e 02

=+d 14003 =
−d 03 =+e 03 =

−e 03

Table 10
Optimal Solutions of Objective Functions and Expected Profit (in 10,000 Rials).

f1s if s = 1 203570,000 ′f 1 245263,700
f1s if s=2 202683,200 f2 0.05333
f1s if s=3 195272,000 f3 9900
expected profit 201052,100 f4 0.1666

Table 11
Activated and Non-Activated CRM Options.

Option A 0 Option D 1
Option B 1 Option E 1
Option C 1 Option F 1

Table 12
Optimal Quantity of Production in Manufacturing and Recycling Plants.

t1 t2

Qplt Tehran p1 3700,000 3700,000
p2 1200,000 1200,000

Q′ p′nt Tehran p′1 19,212 19,209
p′2 12,808 12,806

Isfahan p′1 21,612 21,609
p′2 14,406 14,408

M. Yadollahinia et al. Resources, Conservation & Recycling 138 (2018) 215–228

226

reach the minimum deviations from the target value. To do so, by
changing the values of the deviation weight for the first objective, i.e.
ω1, in the interval (0,1), the deviations from the goals (f4) are calculated
and depicted in Fig. 5. Similarly, the effect of changing ω2 and ω3 on f4
is displayed in Figs. 6 and 7, respectively.

As shown in Figs. 5–7, the magnitude of deviations from the goals
reaches its corresponding maximum values when the value of ω1 is less
than 0.25, value of ω2 is more than 0.9, and value of ω3 is either less
than 0.25 or more than 0.75. This can help DMs and senior managers of
the SC to assign much more appropriate weights to each objective in
order to minimize the deviations from the goals. The amount of the
expected profit in the scenario-based model depends on the scenario
that takes place. This is illustrated in Fig. 8 where each of the three
scenarios is considered to occur with the probability of 0.25, 0.5, and
0.25, respectively. However, the robust counterpart assures that re-
gardless of the scenarios, the expected profit is optimized.

6. Conclusions and further research directions

In recent years, the field of reverse logistics has received more at-
tention from manufacturers from different economic, environmental
and political points of view. Accordingly, considering reverse supply
chains along with forward supply chains has become essential more
than ever. On the other hand, the ever-increasing amount of used tires
brings on serious environmental problems. In addition, the approach
followed to deal with used tires plays an important role in terms of
economic benefits, market demand, etc. In this regard, a comprehensive
and effective planning is needed to collect and recycle end-of-life tires
in an appropriate way.

These were the primary sources of motivation in this paper to pre-
sent a multi-objective, multi-period, multi-product MILP model under
uncertain demands and capacities for the tire industry. In the proposed
model, three objective functions of maximizing total profit, maximizing
customer satisfaction and minimizing distance between collecting fa-
cilities and customers were considered. In this study, the CRM concept
was innovatively incorporated into the SCM concept in order to have a
more customer-centric SC and thus enable the SC to survive and thrive
in the global competitive business environment. To achieve this, dif-
ferent CRM options were defined and the corresponding decisions were
modeled as binary variables considering their estimated costs. To solve
the proposed scenario-based model, it was converted to an equivalent
robust counterpart and a revised multi-choice goal programming
method was applied for obtaining an efficient solution with the three
objective functions of the model.

A case study of the tire industry was conducted including a manu-
facturing plant, two distribution centers and eight main customers in
the existing forward tire SC network. In this case, car and truck tires in
the forward flow and crumb rubber and steel in the backward flow were
considered. In the optimal SC network, obtained by the proposed
model, a collection center, a hybrid center and two recycling plants
were opened. From the obtained results, the first objective function was
fully satisfied. Moreover, sensitivity analysis showed that activating
Option A as a CRM action resulted in a 12.5 percent increase in the total
profit of the chain.

There are some potential future research directions, such as:

• The function that calculated the quantity of the total received used
products from the customers by means of considering customer sa-
tisfaction in the second objective function was assumed to be linear;
however, it can be approximated more accurately using interviews,
questionnaires and other data collection tools.

• In Option A of the CRM options, β could be considered as a decision
variable rather than a parameter.

• Uncertainty in other important parameters of the model can be
formulated.

• Attention must be given to other new and emerging recycling in-
dustries.

References

Amin, S.H., Zhang, G., Akhtar, P., 2017. Effects of uncertainty on a tire closed-loop supply
chain network. Expert Syst. Appl. 73, 82–91.

Ayvaz, B., Bolat, B., Aydın, N., 2015. Stochastic reverse logistics network design for waste
of electrical and electronic equipment. Resour. Conserv. Recycl. 104, 391–404.

Chang, C.-T., 2007. Multi-choice goal programming. Omega 35, 389–396.
Chang, C.-T., 2008. Revised multi-choice goal programming. Appl. Math. Model. 32,

2587–2595.
Charnes, A., Cooper, W.W., 1957. Management models and industrial applications of

linear programming. Manage. Sci. 4, 38–91.
De Souza, C.D.R., D’Agosto, M.D.A., 2013. Value chain analysis applied to the scrap tire

reverse logistics chain: an applied study of co-processing in the cement industry.
Resour. Conserv. Recycl. 78, 15–25.

Dehghanian, F., Mansour, S., 2009. Designing sustainable recovery network of end-of-life
products using genetic algorithm. Resour. Conserv. Recycl. 53, 559–570.

Farina, A., Zanetti, M.C., Santagata, E., Blengini, G.A., 2017. Life cycle assessment applied
to bituminous mixtures containing recycled materials: crumb rubber and reclaimed

Fig. 5. The effect of changing ω1 on f4.

Fig. 6. The effect of changing ω2 on f4.

Fig. 7. The effect of changing ω3 on f4.

Fig. 8. Expected profit in the scenario-based model vs. the robust counterpart.

M. Yadollahinia et al. Resources, Conservation & Recycling 138 (2018) 215–228

227

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0005

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0005

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0010

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0010

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0015

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0020

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0020

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0025

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0025

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0030

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0030

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0030

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0035

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0035

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0040

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0040

asphalt pavement. Resour. Conserv. Recycl. 117 (Part B), 204–212.
Fleischmann, M., Bloemhof-Ruwaard, J.M., Dekker, R., Van Der Laan, E., Van Nunen,

J.A.E.E., Van Wassenhove, L.N., 1997. Quantitative models for reverse logistics: a
review. Eur. J. Oper. Res. 103, 1–17.

Grönroos, c., 2000. service management and marketing: a customer relationship man-
agement approach. Wiley.

Hatefi, S.M., Jolai, F., 2014. Robust and reliable forward–reverse logistics network design
under demand uncertainty and facility disruptions. Appl. Math. Model. 38,
2630–2647.

Hao Yu, H., Solvang, W.D., 2017. A new two-stage stochastic model for reverse logistics
network design under government subsidy and low-carbon emission requirement,
Industrial Engineering and Engineering Management (IEEM). IEEE International
Conference on 90–94.

Kara, S.S., Onut, S., 2010. A stochastic optimization approach for paper recycling reverse
logistics network design under uncertainty. Int. J. Environ. Sci. Technol. 7 (4),
717–730.

Kotler, P., Keller, K.L., 2012. Marketing Management. Prentice Hall, Upper Saddle
River, N.J.

Kracklauer, A.H., Mills, D.Q., Seifert, D., Barz, M., 2004. The integration of supply chain
management and customer relationship management. In: Kracklauer, A.H., Mills,
D.Q., Seifert, D. (Eds.), Collaborative Customer Relationship Management: Taking
CRM to the Next Level. Berlin, Heidelberg, Springer Berlin Heidelberg.

Leung, S.C.H., Wu, Y., Lai, K.K., 2002. A robust optimization model for a cross-border
logistics problem with fleet composition in an uncertain environment. Math. Comput.
Model. 36, 1221–1234.

Liu, F., You, Y., 2011. Study and explores on CRM based on the supply chain integration.
Manag. Sci. Eng. 5, 1.

Mohamadpour Tosarkani, B., Hassanzadeh Amin, S., 2018. A possibilistic solution to
configure a battery closed-loop supply chain: Multi-objective approach. Expert Syst.
Appl. 92, 12–26.

Mulvey, J.M., Ruszczyński, A., 1995. A new scenario decomposition method for large-
scale stochastic optimization. Oper. Res. 43, 477–490.

Mulvey, J.M., Vanderbei, R.J., Zenios, S.A., 1995. Robust optimization of large-scale
systems. Oper. Res. 43, 264–281.

Paixao, M., Souza, J., 2015. A robust optimization approach to the next release problem
in the presence of uncertainties. J. Syst. Softw. 103, 281–295.

Paydar, M.M., Babaveisi, V., Safaei, A.S., 2017. Engine oil closed- loop supply chain
considering collection risk. Comput. Chem. Eng. 104, 38–55.

Pedram, A., Yusoff, N.B., Udoncy, O.E., Mahat, A.B., Pedram, P., Babalola, A., 2017.
Integrated forward and reverse supply chain: a tire case study. Waste Manag. 60,
460–470.

Presti, D.L., 2013. Recycled tyre rubber modified bitumens for road asphalt mixtures: a
literature review. Constr. Build. Mater. 49, 863–881.

Rahimi, M., Ghezavati, V., 2018. Sustainable multi-period reverse logistics network de-
sign and planning under uncertainty utilizing conditional value at risk (CVaR) for
recycling construction and demolition waste. J. Cleaner Prod. 172, 1567–1581.

Realff, M., Ammons, J.C., Newton, D.J., 2004. Robust reverse production system design
for carpet recycling. IIE Trans. 36 (8), 767–776.

Simic, V., Dabic-Ostojic, S., 2016. Interval-parameter chance-constrained programming
model for uncertainty-based decision making in tire retreading industry. J. Clean.
Prod.

Subulan, K., Taşan, A.S., Baykasoğlu, A., 2015. Designing an environmentally conscious
tire closed-loop supply chain network with multiple recovery options using inter-
active fuzzy goal programming. Appl. Math. Model. 39, 2661–2702.

M. Yadollahinia et al. Resources, Conservation & Recycling 138 (2018) 215–228
228

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0040

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0045

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0045

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0045

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0050

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0050

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0055

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0055

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0055

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0060

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0060

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0060

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0060

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0065

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0065

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0065

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0070

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0070

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0075

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0075

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0075

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0075

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0080

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0080

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0080

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0085

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0085

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0090

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0090

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0090

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0095

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0095

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0100

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0100

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0105

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0105

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0110

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0110

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0115

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0115

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0115

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0120

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0120

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0125

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0125

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0125

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0130

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0130

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0135

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0135

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0135

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0140

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0140

http://refhub.elsevier.com/S0921-3449(18)30269-6/sbref0140

  • Tire forward and reverse supply chain design considering customer relationship management
  • Introduction
    Literature review
    Previous researches in product recovery considering uncertainty
    Previous researches in used tires
    Problem explanation
    Problem definition
    Problem formulation
    Notations
    Objective functions
    Constraints
    Balance constraints
    Capacity constraints
    Shipping-linking constraints
    CRM constraint
    Logical constraints
    Linearization

    Solution procedure
    Step1: robust optimization
    Step2: multi-objective methodology: RMCGP
    Case study
    Description and input data
    Computational results
    Sensitivity analysis
    Conclusions and further research directions
    References

Expert paper writers are just a few clicks away

Place an order in 3 easy steps. Takes less than 5 mins.

Calculate the price of your order

You will get a personal manager and a discount.
We'll send you the first draft for approval by at
Total price:
$0.00

Order your essay today and save 30% with the discount code ESSAYHELP