Application of Ant Colony Algorithm and Genetic Algorithm in Multi-Objective Dynamic Equalization Scheduling of AGVs

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Heuristic genetic algorithm improves multi-objective scheduling for AGVs, indicating better efficiency in warehousing.

Minimizing AGV picking time was achieved using a single-objective constraint model and path planning.
The ant colony algorithm and genetic algorithm were employed for optimized path planning and multi-objective scheduling.
The study constructed a warehouse model to enhance AGV task allocation and maximize efficiency.
The approach highlights the need for advanced algorithms in the optimization of unmanned warehousing systems.

Lu et al. (2025) studied this question.

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