合作型协同进化遗传算法求解分布式柔性作业车间调度问题

Cooperative co-evolutionary genetic algorithm for distributed flexible job shopscheduling problem

  • 摘要: 针对以最小化最大完工时间为优化目标的分布式柔性作业车间调度问题,提出一种合作型协同进化遗传算法。采用工厂分配和工序排序解耦编码,基于机器负荷解码并基于工厂负荷初始化种群,使算法在较优的解空间内迭代搜索。利用分而治之的思想,将问题分解为多个子问题,通过随机协同机制促进子种群协同进化并提高全局搜索能力。使用基于关键工厂的多重局部扰动策略,提高算法的局部开发能力。在基准实例上进行实验,并与其他算法进行对比,验证了所提算法的有效性。

     

    Abstract: A cooperative co-evolutionary genetic algorithm is proposed for a distributed flexible job shop scheduling problem with the optimization objective of minimizing the maximum completion time. A decoupled encoding of factory assignment and operation sequencing is used, based on machine load decoding as well as initializing the population based on factory load, so that the algorithm iteratively searches in a better solution space. Using the divide-and-conquer idea, the problem is decomposed into multiple sub-problems, and a random collaboration mechanism is used to promote the subpopulations to co-evolve and improve the global exploration capability. Multiple local perturbation strategy based on key factory is used to improve the local exploitation capability. Experiments are conducted on benchmark instances and compared with other algorithms to verify the effectiveness of the proposed algorithm.

     

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