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Multi-objective sustainable supply chain network optimization based on chaotic particle-Ant colony algorithm.

Tianrui Zhang1, Wei Xie1, Mingqi Wei2

  • 1School of Mechanical Engineering, Shenyang University, Shen Yang, China.

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|July 10, 2023
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Summary
This summary is machine-generated.

This study optimizes sustainable supply chain networks by integrating economic, environmental, and social factors. A novel algorithm, Chaotic Particle Ant Colony (PSCACO), effectively balances multiple objectives for improved supply chain management.

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Area of Science:

  • Operations Research
  • Environmental Science
  • Management Science

Background:

  • Sustainable supply chain network design requires balancing economic, environmental, and social objectives.
  • Existing models often fail to comprehensively address diverse environmental impacts beyond carbon emissions.
  • Multi-objective optimization challenges include solution quality assessment and algorithm convergence.

Purpose of the Study:

  • To develop an optimal design for sustainable supply chain networks.
  • To integrate economic (cost minimization), environmental (pollution minimization), and social (labor maximization) objectives.
  • To propose a novel algorithm for solving complex multi-objective supply chain problems.

Main Methods:

  • Formulation of a mixed-integer programming model for supply chain efficiency maximization.
  • Construction of a multi-objective fuzzy affiliation function to evaluate solution satisfaction.
  • Development and application of the Chaotic Particle Ant Colony (PSCACO) algorithm.

Main Results:

  • The PSCACO algorithm demonstrates superior convergence compared to MOPSO, CACO, and NSGA-II.
  • The model effectively integrates economic, environmental (including wastewater, waste, and solid waste emissions), and social benefits.
  • The proposed approach verifies the effectiveness and feasibility of PSCACO for multi-objective optimization.

Conclusions:

  • The PSCACO algorithm offers a robust solution for optimizing sustainable supply chain networks.
  • Integrating diverse economic, environmental, and social factors leads to more comprehensive supply chain design.
  • This research provides a new feasible solution for sustainable supply chain management challenges.