A fuzzy multi-objective optimization model for sustainable reverse logistics network design
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Ecological Indicators 67 (2016) 753–768
Contents lists available at ScienceDirect
Ecological Indicators
j o u r n a l h o m e 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 sustainable reverse
logistics network design
Kannan Govindan a,∗, Parichehr Paam b , Amir-Reza Abtahi b,∗
a Centre for Sustainable Engineering Operations Management, Department of Technology and Innovation,
University of Southern Denmark, Odense, Denmark
b Department Information Technology Management, Faculty of Management, Kharazmi University, Tehran, Iran
a r t i c l e i n f o
Article history:
Received 30 July 2015
Received in revised form 26 February 2016
Accepted 5 March 2016
Available online 25 April 2016
Keywords:
Reverse logistics
Sustainability
Social responsibility
Fuzzy mathematical programming
Multi-objective metaheuristic algorithm
Epsilon-constraint method
a b s t r a c t
Decreasing the environmental impact, increasing the degree of social responsibility, and considering
the economic motivations of organizations are three significant features in designing a reverse logistics
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
researchers and practitioners. In this paper, we try to address this comprehensive approach by using
indicators for measurement of aforementioned aspects and by applying fuzzy mathematical program-
ming to design a multi-echelon multi-period multi-objective model for a sustainable reverse logistics
network. To reflect all aspects of 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 mathematical programming 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 computational time using analysis of variance. Finally,
in order to indicate the applicability of the suggested model and the practicality of the proposed solution
procedure, the model has been implemented in a medical syringe recycling system. The results reveal
that the suggested MOPSO algorithm overtakes epsilon-constraint method from the aspects of quality
of the solutions as well as computational time. Proper use of the proposed process could help managers
efficiently manage the flow of recycled products with regard to environmental and social considerations,
and the process offers a sustainable competitive advantage to 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 regarded as a managerial priority (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 management of these operations can improve the sys-
tem of distribution and collection of goods and 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
∗ Corresponding author. Tel.: +45 65503188.
E-mail addresses: kgov@iti.sdu.dk (K. Govindan), abtahi@khu.ac.ir
(A.-R. Abtahi).
logistical activities that are related to resource reduction, 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 researchers and industrial practitioners to find a solution for
these problems.
One of the most important objectives facing the researchers is
to design a RL network that can minimize the costs, and simulta-
neously can consider green and social issues. Incorporation of green
strategies and 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.
Contents lists available at ScienceDirect
Ecological Indicators
j o u r n a l h o m e 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 sustainable reverse
logistics network design
Kannan Govindan a,∗, Parichehr Paam b , Amir-Reza Abtahi b,∗
a Centre for Sustainable Engineering Operations Management, Department of Technology and Innovation,
University of Southern Denmark, Odense, Denmark
b Department Information Technology Management, Faculty of Management, Kharazmi University, Tehran, Iran
a r t i c l e i n f o
Article history:
Received 30 July 2015
Received in revised form 26 February 2016
Accepted 5 March 2016
Available online 25 April 2016
Keywords:
Reverse logistics
Sustainability
Social responsibility
Fuzzy mathematical programming
Multi-objective metaheuristic algorithm
Epsilon-constraint method
a b s t r a c t
Decreasing the environmental impact, increasing the degree of social responsibility, and considering
the economic motivations of organizations are three significant features in designing a reverse logistics
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
researchers and practitioners. In this paper, we try to address this comprehensive approach by using
indicators for measurement of aforementioned aspects and by applying fuzzy mathematical program-
ming to design a multi-echelon multi-period multi-objective model for a sustainable reverse logistics
network. To reflect all aspects of 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 mathematical programming 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 computational time using analysis of variance. Finally,
in order to indicate the applicability of the suggested model and the practicality of the proposed solution
procedure, the model has been implemented in a medical syringe recycling system. The results reveal
that the suggested MOPSO algorithm overtakes epsilon-constraint method from the aspects of quality
of the solutions as well as computational time. Proper use of the proposed process could help managers
efficiently manage the flow of recycled products with regard to environmental and social considerations,
and the process offers a sustainable competitive advantage to 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 regarded as a managerial priority (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 management of these operations can improve the sys-
tem of distribution and collection of goods and 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
∗ Corresponding author. Tel.: +45 65503188.
E-mail addresses: kgov@iti.sdu.dk (K. Govindan), abtahi@khu.ac.ir
(A.-R. Abtahi).
logistical activities that are related to resource reduction, 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 researchers and industrial practitioners to find a solution for
these problems.
One of the most important objectives facing the researchers is
to design a RL network that can minimize the costs, and simulta-
neously can consider green and social issues. Incorporation of green
strategies and 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.
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