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A genetic algorithm with two-step rank-based encoding for closed-loop supply chain network design.

Bowen Ding1, Zhaobin Ma1, Shuoyan Ren1

  • 1School of Artificial Intelligence and Computer Science, and Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi 214122, China.

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Summary
This summary is machine-generated.

This study introduces a genetic algorithm with two-step rank-based encoding (GA-TRE) to optimize closed-loop supply chain (CLSC) networks for increased enterprise profits. GA-TRE effectively handles complex constraints and large scales, outperforming other methods.

Keywords:
closed-loop supply chain network designconstrained optimizationgenetic algorithmlarge-scale optimization

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Sustainable Development

Background:

  • Closed-loop supply chains (CLSCs) are crucial for sustainable development and enterprise profitability.
  • Optimizing CLSC networks is complex due to large scales and multiple constraints.

Purpose of the Study:

  • To develop a general CLSC model for maximizing enterprise profits.
  • To determine optimal transportation routes and delivery volumes within CLSC networks.

Main Methods:

  • A novel genetic algorithm with two-step rank-based encoding (GA-TRE) was developed.
  • GA-TRE incorporates specialized encoding, improved genetic operators (crossover, mutation), and adaptive population mechanisms.
  • Route planning, feasibility prediction, and greedy delivery volume setting are key components.

Main Results:

  • GA-TRE demonstrated superior performance compared to six heuristic algorithms.
  • The proposed algorithm achieved better solutions, particularly for large-scale CLSC instances.
  • Efficiency and effectiveness in handling complex constraints were validated.

Conclusions:

  • GA-TRE provides an efficient and effective solution for optimizing complex CLSC networks.
  • The algorithm enhances profit maximization and supports sustainable business practices.
  • This approach offers a significant advancement in CLSC network optimization.