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An Improved Migratory Birds Optimization Algorithm for Closed- Loop Supply Chain Network Planning in a Fuzzy

Yangjun Ren1, Qiong Chen2, Yui-Yip Lau3

  • 1School of Economics and Management, Changzhou Vocational Institute of Textile and Garment, Changzhou, China.

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|June 27, 2024
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Summary
This summary is machine-generated.

This study introduces a fuzzy programming model and a novel Migratory Birds Optimization Algorithm for uncertain closed-loop supply chain network design. The approach effectively minimizes costs and enhances decision-making for supply chain stakeholders.

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Optimization

Background:

  • Closed-loop supply chain networks offer economic and environmental benefits but face significant uncertainties in demand, costs, and capacities.
  • Traditional network design models struggle to adequately address these inherent uncertainties.
  • Effective management of these uncertainties is crucial for optimizing supply chain performance.

Purpose of the Study:

  • To develop a novel fuzzy programming model for closed-loop supply chain network design that incorporates uncertainty.
  • To propose an efficient optimization algorithm, the Migratory Birds Optimization Algorithm with a new product source encoding scheme, to solve the model.
  • To minimize the total cost of the network, including facility location and transportation route selection.

Main Methods:

  • A fuzzy programming model based on a credibility measure for handling uncertain parameters.
  • A Migratory Birds Optimization Algorithm featuring a novel product source encoding scheme inspired by product packaging and delivery order information.
  • Extensive testing using 35 examples, comparing the proposed algorithm against exact methods (LINGO) and other metaheuristics (GA, ACO, SA).

Main Results:

  • The proposed Migratory Birds Optimization Algorithm consistently provides optimal or near-optimal solutions for large-scale problems within acceptable computational times.
  • The fuzzy programming model effectively handles uncertainties, and sensitivity analysis confirms its suitability and yields valuable managerial insights.
  • The novel encoding scheme improves the likelihood of heuristic algorithms finding optimal solutions compared to traditional methods.

Conclusions:

  • The developed fuzzy programming model and Migratory Birds Optimization Algorithm offer a robust and efficient approach for designing uncertain closed-loop supply chain networks.
  • The research provides a scientifically supported framework for supply chain enterprises and stakeholders to improve network design and cost minimization.
  • The findings highlight the importance of advanced optimization techniques in managing supply chain complexities and uncertainties.