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A coevolutionary algorithm based on the auxiliary population for constrained large-scale multi-objective supply chain

Xin Zhang1,2, Zhaobin Ma1, Bowen Ding1

  • 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 new model for optimizing complex supply chain networks. The proposed algorithm effectively minimizes costs and maximizes customer satisfaction, outperforming existing methods on large-scale problems.

Keywords:
coevolutionary algorithmconstrained optimizationlarge-scale optimizationmulti-objective optimizationsupply chain network

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Computational Intelligence

Background:

  • Supply chain network optimization is crucial for enterprise efficiency but faces increasing complexity.
  • Existing models often struggle with multiple objectives and real-world constraints.

Purpose of the Study:

  • To propose a constrained large-scale multi-objective supply chain network (CLMSCN) optimization model.
  • To develop a novel coevolutionary algorithm based on an auxiliary population (CAAP) to solve the CLMSCN problem.

Main Methods:

  • Developed a CLMSCN model to minimize total operation cost and maximize customer satisfaction under capacity constraints.
  • Proposed the CAAP algorithm utilizing two populations: one for the constrained problem and one for the unconstrained version.
  • Implemented a linear repair operator to enhance the feasibility of solutions generated by the primary population.

Main Results:

  • The CAAP algorithm demonstrated superior performance compared to other algorithms in experimental validation.
  • The algorithm showed particular effectiveness on large-scale supply chain network instances.
  • Experimental results on randomly generated instances confirmed the algorithm's validity.

Conclusions:

  • The CAAP algorithm is an effective approach for solving constrained large-scale multi-objective supply chain network optimization problems.
  • The proposed method offers a significant improvement over existing algorithms, especially for complex, large-scale scenarios.
  • This research contributes a valuable tool for enhancing supply chain operations and management.