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An Optimization Method of Production-Distribution in Multi-Value-Chain.

Shihao Wang1,2, Jianxiong Zhang1,2, Xuefeng Ding1,2

  • 1College of Computer Science, Sichuan University, Chengdu 610065, China.

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|February 28, 2023
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Summary
This summary is machine-generated.

This study introduces a multi-value-chain (MVC) collaboration network model to optimize enterprise production and distribution. An enhanced genetic algorithm (ERGA) improves efficiency and reduces costs in complex supply chains.

Keywords:
genetic algorithmhigh-dimensional decision spacemulti-value-chain collaborationoptimal planningproduction-distribution

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Computational Intelligence

Background:

  • Value chain collaboration enhances enterprise competitiveness by reducing costs and increasing efficiency.
  • Existing models combining vertical and horizontal collaboration struggle with task assignment and node constraints in production-distribution processes.
  • Dynamic enterprise alliances require advanced models for multi-value-chain (MVC) collaboration.

Purpose of the Study:

  • To model and optimize the MVC collaboration process within enterprise dynamic alliances.
  • To develop an optimization model for the MVC collaboration network focusing on minimizing production-distribution costs.
  • To address the challenges of high-dimensional decision spaces and complex constraints in multi-level production-distribution scenarios.

Main Methods:

  • Constructed an MVC collaboration network optimization model with cost minimization as the objective and delivery cycle/task quantity as constraints.
  • Employed a genetic algorithm to solve the high-dimensional optimization problem, adjusting gene constraints for node collaboration.
  • Proposed two chromosome coding methods (staged and integrated) and an enhanced roulette genetic algorithm (ERGA) with improved elite retention.

Main Results:

  • ERGA demonstrated superior performance over simple genetic algorithm (SGA) and strengthened elitist genetic algorithm (SEGA) in terms of time cost and optimization results.
  • Comparative experiments and population evolution analysis confirmed ERGA's effectiveness.
  • The proposed coding methods and selection operators in ERGA contribute to its enhanced capabilities.

Conclusions:

  • ERGA provides a more efficient and effective solution for optimizing MVC collaboration networks compared to existing genetic algorithms.
  • The developed model and ERGA are adaptable to various production-distribution environments, offering broad applicability.
  • This research offers a robust framework for improving collaboration and efficiency in complex, multi-level supply chains.