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    王全武, 徐震浩, 顾幸生. 基于头脑风暴算法的多处理机组合生产批量调度问题[J]. 华东理工大学学报(自然科学版), 2022, 48(5): 685-695. DOI: 10.14135/j.cnki.1006-3080.20210427005
    引用本文: 王全武, 徐震浩, 顾幸生. 基于头脑风暴算法的多处理机组合生产批量调度问题[J]. 华东理工大学学报(自然科学版), 2022, 48(5): 685-695. DOI: 10.14135/j.cnki.1006-3080.20210427005
    WANG Quanwu, XU Zhenhao, GU Xingsheng. Multi-Processor Combined Production Batch Scheduling Problem Based on Brain Storm Optimization Algorithm[J]. Journal of East China University of Science and Technology, 2022, 48(5): 685-695. DOI: 10.14135/j.cnki.1006-3080.20210427005
    Citation: WANG Quanwu, XU Zhenhao, GU Xingsheng. Multi-Processor Combined Production Batch Scheduling Problem Based on Brain Storm Optimization Algorithm[J]. Journal of East China University of Science and Technology, 2022, 48(5): 685-695. DOI: 10.14135/j.cnki.1006-3080.20210427005

    基于头脑风暴算法的多处理机组合生产批量调度问题

    Multi-Processor Combined Production Batch Scheduling Problem Based on Brain Storm Optimization Algorithm

    • 摘要: 在生产调度领域中,受生产工艺等诸多因素的影响,往往每个生产过程都需要多台机器同时参与加工。同时,待加工的工件数量较多,需要将每种类型的工件进行批量处理,以缩短生产周期。本文在作业车间环境下,根据每个加工过程所参与机器的负荷,采用可变分批方案,提出了非混排多处理机组合生产批量调度模型,并结合头脑风暴优化算法,求解出最短加工时间。提出了一种改进的头脑风暴优化算法,引入贪婪思想与动态讨论机制,讨论次数随着算法的迭代而自适应变化,将全局搜索与局部搜索相结合,加强了算法的搜索能力。实验结果表明,改进的头脑风暴优化算法与基本的头脑风暴优化算法相比,求解效率更高,收敛速度更快。

       

      Abstract: In the field of production scheduling, due to the influence of many factors such as production technology, each production process usually requires multiple machines to simultaneously participate in processing. Meanwhile, the number of workpieces to be processed is large, and each type of workpiece needs to be processed in batches for shortening the production cycle. Aiming at the above problems, in a job shop environment, this paper adopts a variable batching scheme according to the load of the machines involved in each processing process, and proposes a non-mixed multi-processor combined production batch scheduling model and integrate the brainstorming algorithm to search the shortest processing time. Moreover, an improved brainstorming algorithm is proposed by introducing greedy thinking and dynamic discussion mechanism. The number of discussions is changed adaptively with the iteration and the global search and local search are utilized to strengthen the search ability of the proposed algorithm. Finally, it is shown via the test results that the improved brainstorming algorithm is more efficient and convergent than the basic brainstorming algorithm.

       

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