Multi-AGV task scheduling based on improved double-chromosome genetic algorithm

Shulei Ge; Tong Yang; Fan Mo

doi:10.1117/12.3054419

20 January 2025 Multi-AGV task scheduling based on improved double-chromosome genetic algorithm

Shulei Ge, Tong Yang, Fan Mo

Author Affiliations +

Proceedings Volume 13515, Fourth International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2024); 135151H (2025) https://doi.org/10.1117/12.3054419
Event: 4th International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2024), 2024, Chongqing, China

Abstract

Current research on multi-AGV task scheduling mainly focuses on minimizing the total running time of multiple AGVs, with less consideration for issues such as AGV wear and cost balancing. This paper proposes a multi-AGV task scheduling optimization method that considers not only the total running time, but also the maximum running time of individual AGVs. The method aims to ensure high overall operational efficiency while improving AGV wear and cost imbalance issues. The method is based on an improved double-chromosome genetic algorithm (IDCGA), adopting a double-layer encoding structure with task chromosomes and AGV chromosomes, and designing an optimized greedy search strategy and population destruction-reconstruction strategy to avoid being trapped in local optima. To improve the computation speed, parallel computing is also employed to accelerate the IDCGA algorithm. Simulation experiments show that when the weight coefficient w is set to 0.9, the method not only has good robustness, but also the fastest computation speed. The simulation experiments also verify that the IDCGA algorithm can obtain better results, with faster running speed and better robustness, compared to the traditional genetic algorithm (GA).

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shulei Ge, Tong Yang, and Fan Mo "Multi-AGV task scheduling based on improved double-chromosome genetic algorithm", Proc. SPIE 13515, Fourth International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2024), 135151H (20 January 2025); https://doi.org/10.1117/12.3054419

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