Solving Multi-robot Task Assignment Problem based on Improved Genetic Algorithm

Aiming at the low efficiency of robot task distribution and unbalanced task distribution in the process of warehouse task scheduling, this paper aims at minimum total task path and balanced task distribution, establishes a warehouse robot scheduling scheme and mathematical model, and proposes an improved genetic algorithm for task allocation. Firstly, greedy algorithm is introduced to optimize the initial population and improve the quality of the initial population. Secondly, the heuristic bidirectional triple crossover operator is designed to expand the search range of the population and improve the accuracy of the optimal solution. Finally, the multi-variant strategy is introduced to solve the problem that the algorithm falls into local optimality. The experimental results show that the improved algorithm has better convergence and balanced distribution ability.

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Publication Details

Journal
Frontiers in Computing and Intelligent Systems
Published
2024-12-29
DOI
https://doi.org/10.54097/sctr7h26
Citations
1
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
0.35

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article

Solving Multi-robot Task Assignment Problem based on Improved Genetic Algorithm

Zhibin Wang, Jianjun Zhu
1 citations
Frontiers in Computing and Intelligent Systems
Advanced Manufacturing and Logistics Optimization
0.35
article

Solving Multi-robot Task Assignment Problem based on Improved Genetic Algorithm

Zhibin Wang, Jianjun Zhu
article en
1 citations

Abstract

Aiming at the low efficiency of robot task distribution and unbalanced task distribution in the process of warehouse task scheduling, this paper aims at minimum total task path and balanced task distribution, establishes a warehouse robot scheduling scheme and mathematical model, and proposes an improved genetic algorithm for task allocation. Firstly, greedy algorithm is introduced to optimize the initial population and improve the quality of the initial population. Secondly, the heuristic bidirectional triple crossover operator is designed to expand the search range of the population and improve the accuracy of the optimal solution. Finally, the multi-variant strategy is introduced to solve the problem that the algorithm falls into local optimality. The experimental results show that the improved algorithm has better convergence and balanced distribution ability.

Frontiers in Computing and Intelligent SystemsVol. 10(3)
Jilin University of Chemical Technology (CN)
People's Government of Jilin Province
Openalex Percentile: Top 32%
Advanced Manufacturing and Logistics Optimization
0.35
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