A fuzzy multi-objective optimization model for sustainable reverse logistics network design

This paper presents a fuzzy multi-objective optimization model for designing a sustainable reverse logistics network. The model considers environmental, social, and economic aspects and their indicators. Fuzzy mathematical programming is used to deal with uncertain parameters, and a customized multi-objective particle swarm optimization algorithm is applied to obtain solutions on the Pareto front. The proposed model is implemented in a medical syringe recycling system to demonstrate its applicability and practicality.

Ecological Indicators 67 (2016) 753–768
Contentslists available at ScienceDirect
Ecological Indicators
j o u rn al h ome p a g e :w w w . e l s e v i e r . c o m / l o c a t e / e c o l i n d
A fuzzy multi-objective optimization model for sustainablereverse
logistics network design
Kannan Govindana,∗, Parichehr Paamb, Amir-Reza Abtahib,∗
a Centrefor SustainableEngineeringOperationsManagement,Departmentof Technologyand Innovation,
Universityof SouthernDenmark,Odense,Denmark
b DepartmentInformationTechnologyManagement,Facultyof Management,KharazmiUniversity,Tehran,Iran
a r t i c l e i n f o
Articlehistory:
Received30 July 2015
Receivedin revised form 26 February 2016
Accepted5 March 2016
Available online 25 April 2016
Keywords:
Reverselogistics
Sustainability
Social responsibility
Fuzzy mathematicalprogramming
Multi-objective metaheuristicalgorithm
Epsilon-constraint method
a b s t r a c t
Decreasingthe environmental impact, increasing the degree of social responsibility, and considering
the economic motivations of organizationsare three significant featuresin designing a reverselogistics
network under sustainability respects.Developing a model, which can simultaneously consider these
environmental, social, and economic aspects and their indicators, is an important problem for both
researchersand practitioners. In this paper, we try to address this comprehensiveapproach by using
indicators for measurementof aforementionedaspectsand by applying fuzzy mathematicalprogram-
ming to design a multi-echelon multi-period multi-objective model for a sustainable reverse logistics
network. To reflect all aspectsof sustainability, we try to minimize the present value of costs, as well
as environmental impacts, and optimize the social responsibility as objective functions of the model. In
order to deal with uncertain parameters,fuzzy mathematicalprogramming is used, and to obtain solu-
tions on Pareto front, a customized multi-objective particle swarm optimization (MOPSO) algorithm is
applied. The validity of the proposed solution procedure has been analyzed in small and large size test
problems based on four comparison metrics and computationaltime using analysis of variance.Finally,
in order to indicate the applicability of the suggestedmodel and the practicalityof the proposedsolution
procedure,the model has been implemented in a medical syringe recycling system. The results reveal
that the suggestedMOPSO algorithm overtakesepsilon-constraint method from the aspectsof quality
of the solutions as well as computationaltime. Proper use of the proposed processcould help managers
efficiently managethe flow of recycledproducts with regardto environmentaland social considerations,
and the processoffers a sustainablecompetitive advantageto corporations.
© 2016 Elsevier Ltd. All rights reserved.
1. Introduction
Reverse logistics (RL) is one of the significant areas discussed
in subjects related to logistics and management of supply chain
in various industries. Because of the great effects on customer
relationships, reverse logistics and logistics related to operational
capabilities should be regardedas a managerialpriority (Liu, 2014;
Bouzon et al., 2016). RL is a general term that covers a wide area,
including all operations related to re-using of goods and materials.
The efficient managementof theseoperations can improve the sys-
tem of distribution and collection of goodsand materials.Generally,
the aim of RL is to manage reverse currents; that is, the backward
currents in the supply chain (Álvarez et al., 2007). RL includes all
∗ Correspondingauthor. Tel.: [phone].
E-mail addresses:[e-mail](K. Govindan), [e-mail]
(A.-R. Abtahi).
logistical activities that are related to resourcereduction,recycling,
replacing,re-using the materials,and dissolving the wastes (Stock,
1992).
Over the past decade, companies have been challenged with
the complicated issues of customer returns; accordingly,RL devel-
oped much more as a set of models and techniques to manage
these issues. The rapid growth of RL activities has heightened the
levels of social and environmental degradation (Henriques and
Sadorsky,1996) and, consequently,has drawn the attention of aca-
demic researchersand industrial practitioners to find a solution for
these problems.
One of the most important objectives facing the researchersis
to design a RL network that can minimize the costs, and simulta-
neously can consider greenand social issues.Incorporation of green
strategiesand sustainability in logistics can be the keys to address
these problems (Murphy and Poist, 2000). In a broader vision, in
addition to providing green products and services for customers,
green logistics (GL) considers the overall logistics of an item’s flow.
http://dx.doi.org/10.1016/j.ecolind.2016.03.017
1470-160X/©2016 Elsevier Ltd. All rights reserved.

754 K. Govindanet al. / EcologicalIndicators67 (2016)753–768
In other words, not only do the entities in the supply chain have
to be green in their own logistics operations, but also they have to
cooperatewith each other with regard to green considerations.In
this approach,RL is a primary issue of GL, and cannot be designed
by simply considering economic aspects.Hence, in a sustainable
way, environmental factors have to be considered as well (Zhou
et al., 2000; Björklund et al., 2012; Hervani et al., 2005; Zhanget al.,
2015). In addition, companies must care about their social respon-
sibility. This implies that companies, besides making profit, have
to think about conformity with legal necessities,ethical principles,
and esteemfor people and communities in all of their activities (Pai
et al., 2015).
Thus, the term sustainability is used when environmental and
social factors are taken into account in addition to economic
aspects.Seuring and Muller (2008) defined sustainability as “the
designing and employing human systemsas well as industrial sys-
tems in order to use natural resources and to make sure that the
normal cycles do not reduce the quality of life and future eco-
nomic opportunities and also do not have any negativeimpact on
social conditions, human health, and ecosystem.”Every company
and their individuals needto developthemselveswith regardto the
health and safetyof all creatures,to a cleanerenvironment,and help
to ensure a safe and balanced society (Kotzee and Reyers,2016).
Today’scompanies take a range of different approachesin their
pursuit of sustainabilityas they attemptto maketheir supply chains
more responsive to the environment and society. Effective RL pro-
grams have great potential in helping them attain these goals,
because sustainability and RL are interconnected.Hence, design-
ing the supply chains with optimized RL can help them enhance
resource recovery,reduce returns, integrate shipments, and adjust
transportation administration (Lee and Lam, 2012).
Many researchers trying to explore the relationships linking
sustainability and green supply chain in RL (Govindan, 2015;
Rostamzadeh et al., 2015). Most of the previous research has
focused on social and green issues through closed-loop supply
chain, but integrating RL in the design of green and sustain-
able supply chains has rarely been considered (Govindan et al.,
2015a,b,c,2016; Govindan and Cheng, 2015). In a quantitative
approach, this integration can be done by adding some decision
variables, objective functions, and constraints into mathematical
models (Govindan et al., 2015b; Soleimani and Govindan, 2015).
Therefore, the contribution of this paper is to integrate the green
and sustainability issues in RL. In this study, a sustainable reverse
logistics network design (SRLND) is presented.The minimization
of the present value of costs, the minimization of environmen-
tal impacts, and the maximization of social responsibility are the
objective functions to consider the three aspectsof sustainability.
Social responsibility is considered as increasing the career oppor-
tunities and reducing harms at work. In order to minimize the
environmental impacts, the eco-indicator 99 methodology is used,
which is a way for estimatingthe environmentalimpactsof a supply
chain network (Pishvaeeand Razmi, 2012; Pishvaeeet al., 2012).
Network design problems are classified as NP-Hard problems
and the duration of the solution processis increasedexponentially
according to the size of the problem (Aras et al., 2008; Fattahi et al.,
2015). Consequently,the problem of multi-objective RL network
design is an NP-Hard problem. In order to solve this problem in
an acceptablelength of time, meta-heuristic algorithms should be
used. The result of solving the multi-objective RL network design
problem with a meta-heuristic algorithm is the production of non-
dominated Pareto optimal solutions for decision-makers.By using
meta-heuristic algorithms, all possible solutions for the problem
are obtained and the decision-makerscan make their final decision
based on comparison metrics and comprehensivedata.
The rest of this paper is organized as follows. In Section 2, the
literature in this area of research is reviewed. In Section 3, the
problem is defined and the model is formulated. In Section 4, the
solving methodology and comparison metrics are discussed.Sec-
tion 5 is dedicatedto experimentalresults. Finally, in Section 6, the
conclusions are presentedand suggestionsfor future research are
provided.
2. Literature review
In this paper, we attempt to propose a model for a RL net-
work designproblem, regardinggreenand sustainability issuesand
use a meta-heuristic algorithm to solve the model. Consequently,
the focus of the literature survey in this study is subdivided into
three sections: (1) RL mathematical models for supply chain (SC);
(2) Green and sustainable RL and SC; and (3) The applications of
meta-heuristic algorithms in RL and SC.
2.1. RL mathematicalmodelsfor supplychain
In this section, some RL mathematical models for SC and the
solving methodologies have been reviewed. Fleischmann et al.
(1997) did one of the first studies on various characteristics and
improvements in RL system.They separatedthe subject into three
major parts: production management,inventory management,and
distribution management.Then they elaborated on the implica-
tions of re-using efforts per eachpart, studied the models suggested
in the previous researches, and offered several suggestions for
future researchers. Minner (2001) mixed internal and external
product returns and their improvement with the problem of cer-
tainty inventory control in the SC. He then solved the problem
by a concave minimum optimization method and stated that the
re-using of products creates the surplus inventory. He concluded
that when the complete concurrenceamong various supply condi-
tions is expensive(considering the characteristicsof SC in terms of
processingtime, level of services,and inventory costs),the creation
of surplus inventory is inevitable.
In solving the problem of reverse logistics recycling balance,
Chen (2012) mainly focused on the balance in conditions that the
prices of the market and recyclingflows haveinteractiveeffectsand
the flows of input and output recycled materials in the SC are not
balanced.Leeand Chan(2009) offered an RL network basedon radio
frequencyidentification (RFID) technology.Their aim was to find a
model for the optimization of product output. They suggestedthat
RFID could be used for the counting of stored items in storagepoints
and for sending signals to the returning center.Das and Chowdhury
(2012) offered a recycling, logistics model for various electronic
product wastes in order to minimize the overall processing costs.
Their model consisted of four recycling phases: collection, separa-
tion, recycling, and repair. The final site included a dumping point,
primary market, and secondarymarket. They found that the trans-
portation costs constitutea major part of recycling costs.Therefore,
they concludedthat the reduction of transportationcostsis the best
way to reduce the overall costs of the system.
Nikolaou et al. (2013) presented a combinatorial model for
the social responsibility of companies and sustainability in the RL
system on the basis of a complete operational framework. Their
framework included combinatory mathematical indices for the
evaluation of social responsibility in RL. Their work can enhance
the companies’ performance in social responsibility throughout
reverse logistics processes. Ramezani et al. (2013) developed a
model to design a forward and reverse asymmetric logistics sys-
tem. They suggesteda processfor optimizing the quality, profit, and
customer responsivenessas the objective functions in their model.
Suyabatmaz et al. (2014) presented two modeling methods
for the stochastic RL system design to handle the uncertain-
ties in the problem. One addressed the design of a distribution
network and another considered the development of a generic

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