Multi-objective Design of Sustainable Closed-loop Supply Chain Considering Social Benefits: Metaheuristic Optimization Approaches

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

2 School of Business, Innovation and Sustainability, Halmstad University, 301 18 Halmstad, Sweden

10.22070/jqepo.2024.17510.1255

Abstract

 In line with growing global concerns regarding environmental and social issues, supply chain
corporations are improving their environmental and social performances. The optimal design of a closedloop supply network must conceive various aspects, leading to a multi-objective problem. This study develops
a mixed-integer linear programming model to provide an integrated supply network with a particular focus
on sustainability. Besides cost efficiency, energy consumption, and job creation are incorporated as
additional objective functions. This article uniquely introduces the training of supply chain employees as part
of the developed model to address social responsibility. The Non-Dominated Sorting Genetic Algorithm-II
(NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO) are employed to solve the multiobjective problem. The numerical examples for cost and energy values are based on real data. The results
demonstrate the significant effect of returned product recovery on cost reduction in the network and changes
in energy consumption at different levels. NSGA-II and MOPSO yield a set of optimal solutions that increase
the flexibility of decision-makers. Indeed, a set of Pareto solutions reveals a conflict between the objective
functions and allows the network to be highly effective in decision-making under different conditions and
policies. 

Keywords

References

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Journal of Quality Engineering and Production Optimization
Volume 9, Issue 1
May 2024
Pages 149-186

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