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Research on multi-agent genetic algorithm based on tabu search for the job shop scheduling problem

机译：基于禁忌搜索的车间作业调度问题多智能体遗传算法研究

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摘要

The solution to the job shop scheduling problem (JSSP) is of great significance for improving resource utilization and production efficiency of enterprises. In this paper, in view of its non-deterministic polynomial properties, a multi-agent genetic algorithm based on tabu search (MAGATS) is proposed to solve JSSPs under makespan constraints. Firstly, a multi-agent genetic algorithm (MAGA) is proposed. During the process, a multi-agent grid environment is constructed based on characteristics of multi-agent systems and genetic algorithm (GA), and a corresponding neighbor interaction operator, a mutation operator based on neighborhood structure and a self-learning operator are designed. Then, combining tabu search algorithm with a MAGA, the algorithm MAGATS are presented. Finally, 43 benchmark instances are tested with the new algorithm. Compared with four other algorithms, the optimization performance of it is analyzed based on obtained test results. Effectiveness of the new algorithm is verified by analysis results.
机译：解决车间作业调度问题（JSSP）对于提高企业的资源利用率和生产效率具有重要意义。鉴于其不确定性的多项式性质，提出了一种基于禁忌搜索的多智能体遗传算法（MAGATS），以解决制造期约束下的JSSP问题。首先，提出了一种多智能体遗传算法。在此过程中，根据多智能体系统的特点和遗传算法构建了多智能体网格环境，并设计了相应的邻居交互算子，基于邻域结构的变异算子和自学习算子。然后，结合禁忌搜索算法与MAGA，提出了算法MAGATS。最后，使用新算法测试了43个基准实例。与其他四种算法相比，根据获得的测试结果分析了算法的优化性能。分析结果验证了新算法的有效性。

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