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Integrating multi-agent reinforcement learning and 3D A* search for facility layout problem considering connector-assembly

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  • Qiaoyu Zhang

    (Dalian University of Technology)

  • Yan Lin

    (Dalian University of Technology)

Abstract

The paper proposes an innovation method of integrating a multi-agent reinforcement learning and 3D A* search, , to tackle the facility layout problem with the goal of minimizing connector-assembly cost, including wires and pipes, in a finite three-dimensional space subjected to complex constraints. The mathematical model is established through the bounding boxes and grids, and an improved 3D A* algorithm is used to evaluate the route quality of connectors. After defining facility states in the ranking way, the position and orientation of each facility are iteratively solved by the multi-agent reinforcement learning, Wolf Policy Hill-Climbing (Wolf-PHC), with three actions of horizontal movement, rotation and swap. Then a pure water manufacturing system has been taken as a case to carry out a series of combination comparative experiments with other approaches (Dijkstra; Ant colony optimization; Genetic algorithm) used in the relevant literatures and the parameter values of these approaches have been sampled by Latin Hypercube. The results show that the stability, efficiency and solving quality of are better.

Suggested Citation

  • Qiaoyu Zhang & Yan Lin, 2024. "Integrating multi-agent reinforcement learning and 3D A* search for facility layout problem considering connector-assembly," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3393-3418, October.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:7:d:10.1007_s10845-023-02209-x
    DOI: 10.1007/s10845-023-02209-x
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    References listed on IDEAS

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