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Multi-AGV path planning with double-path constraints by using an improved genetic algorithm

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  • Zengliang Han
  • Dongqing Wang
  • Feng Liu
  • Zhiyong Zhao

Abstract

This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, double-path constraints of both minimizing the total path distance of all AGVs and minimizing single path distances of each AGV are exerted, gaining the optimal shortest total path distance. The simulation results show that the total path distance of all AGVs and the longest single AGV path distance are shortened by using the improved genetic algorithm.

Suggested Citation

  • Zengliang Han & Dongqing Wang & Feng Liu & Zhiyong Zhao, 2017. "Multi-AGV path planning with double-path constraints by using an improved genetic algorithm," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-16, July.
    • Handle: RePEc:plo:pone00:0181747
    DOI: 10.1371/journal.pone.0181747
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    Cited by:

    1. Yubang Liu & Shouwen Ji & Zengrong Su & Dong Guo, 2019. "Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-21, December.
    2. Xiaoqiu Shi & Wei Long & Yanyan Li & Dingshan Deng, 2020. "Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-23, May.
    3. Vinícius Antonio Battagello & Nei Yoshihiro Soma & Rubens Junqueira Magalhães Afonso, 2020. "Computational load reduction of the agent guidance problem using Mixed Integer Programming," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-45, June.
    4. Yuanying Cao & Xi Fang, 2023. "Optimized-Weighted-Speedy Q-Learning Algorithm for Multi-UGV in Static Environment Path Planning under Anti-Collision Cooperation Mechanism," Mathematics, MDPI, vol. 11(11), pages 1-28, May.

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