A hybrid metaheuristic algorithm for data driven leagile sustainable closed-loop supply chain modeling under disruption risk

Document Type : Article

Authors

1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Department of Industrial Engineering, Khatam University, Tehran, Iran

Abstract

In the current situation,taking into consideration the environmental and social issues are related with the production and distribution of products in supplychain systems,due tothe global concerns related with emitting lots of greenhouse gaseswithin the manufacturing process and overlooking the major needs of publicThis paper proposes a newmultiobjectivemodel in the area ofclosed loop supplychainproblem integrated with lot sizing by considering lean,agility and sustainability factors simultaneously.In this regard,responsiveness, environmental,social and economic aspects are regarded in the model besides the capacity and service level constraints.Inaddition,strategic and operational backup decisions are developed to increase the resiliency of the system against disruption of the facilities and routs simultaneously.Next,a robust possibilistic programming approach is applied to handle the uncertainty of the model.To increase the responsiveness of the system,a fuzzyc-means clusteringmethod isapplied to select the potential locations based on the proximity to local customers.In the following, a new hybrid metaheuristic algorithm comprised of a PMOPSO algorithm and aMOSEO is developed to deal with large size problems efficiency and to assess the impact of using a single-based initial solution as the income for the second phase of the proposed hybrid algorithm.To ensure about the effectiveness of the proposed hybrid algorithm,the results of this algorithm arecompared with a NSGA-II.

Keywords

References

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Scientia Iranica
Volume 29, Issue 3
Transactions on Industrial Engineering (E)
May and June 2022
Pages 1685-1704

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History

  • Receive Date: 05 July 2019
  • Revise Date: 27 June 2020
  • Accept Date: 14 September 2020

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