A multi-objective optimization model for closed loop supply chain with emphasis on energy factors in transportation

Articles in Press

Document Type : Research Paper

Authors

1 Associate Professor .Department of Industrial Management, Semnan University, Semnan, Iran

2 PhD student in industrial management, Ferdowsi University of Mashhad, Iran

3 M.sc.Managment Department.Binalood Higher Education institue.Mashhad. iran

Abstract

Aim: Today, it is essential to achieve competitive market interests and since attention to environmental issues and reduced raw resources and energy has increased, design of a suitable supply chain network can be a great help in this regard. To this end, in this study, a multi-product closed-loop green supply chain has been designed in uncertain conditions of demand and exchange rates under three different scenarios of stability, growth and stagnation for several periods. The objective functions of this model are the optimization of transportation costs, energy consumption, carbon dioxide emissions and waste minimization.
Research Methodology: In order to validate the problem model, a small-scale numerical example has been solved by LINGO software and the efficiency of the model has been confirmed since the problem is one of the NP-hard problems, genetic meta-heuristic algorithms and refrigeration simulation have been used and coded by Matlab software. In order to integrate the target functions, the weighting method of the targets has been used, the weight of the targets has been chosen according to the experts' opinion.
Conclusion: The results of the algorithms' efficiency show that when we consider the four objective functions at the same time, the genetic algorithm performs better. This paper helps managers to benefit from green transportation and improving environmental performance and reducing costs in the entire supply chain as a complementary strategy in order to gain a sustainable competitive advantage.

Keywords

Main Subjects

Quarterly Journal of Transportation Engineering

Articles in Press, Accepted Manuscript
Available Online from 28 May 2023

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History

  • Receive Date: 17 May 2022
  • Revise Date: 29 January 2023
  • Accept Date: 30 March 2023

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