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作 者:黎增灿 丁林山 管在林[1] LI Zengcan;DING Linshan;GUAN Zailin(School of Mechanical Science&Engineering,Huazhong University of Science&Technology,Wuhan 430074,China)
机构地区:[1]华中科技大学机械科学与工程学院,武汉430074
出 处:《现代制造工程》2024年第4期13-25,共13页Modern Manufacturing Engineering
摘 要:针对以最小化最大完工时间为目标的柔性作业车间分批调度问题(Flexible Job shop Batch Scheduling Problem,FJBSP),提出了一种改进灰狼优化(Improved Grey Wolf Optimization,IGWO)算法,优化对象为工件的分批方案。首次将流体模型应用于FJBSP的求解,提出了一种基于流体模型的解码方法,用于获得更好的子批调度方案;然后改进了狼群的等级制度,避免了算法过早收敛;其次设计了一种全新的适应可变长编码的交叉方法,深入交流2个个体之间的分批信息,增强了算法的搜索能力和稳定性;再次,提出了能够动态更新个体游走率的自适应灰狼游走策略,兼顾了算法的搜索质量和收敛速度;此外,对领头狼使用自适应邻域搜索动态调整每种工件选择各邻域的概率,提高算法的局部搜索能力;最后,设计了9个算例和3组实验,验证了所提出的IGWO算法的有效性和优越性。Aiming at the Flexible Job shop Batch Scheduling Problem(FJBSP)with the goal of minimizing the makespan,an Improved Grey Wolf Optimization(IGWO)algorithm was proposed to optimize the job batching scheme.For the first time,the fluid model was applied to solve FJBSP through proposing a decoding method based on fluid model to get a better batch scheduling scheme.Secondly,the hierarchical system of the wolf pack was improved to avoid premature convergence of the algorithm.Thirdly,a new crossover method that adapts to the variable length code was designed to deeply exchange the batch information between two individuals,and it can enhance the search ability and stability of algorithm.Then,a self-adaptive grey wolf wandering strategy was proposed to dynamically update the individual′s wandering rate,and it can balance the search quality and convergence speed of algorithm.In addition,the self-adaptive neighborhood search was used on the leader wolves to dynamically adjust the selection probability of each neighborhoods for each kind of job and improve the local search ability of the algorithm.Finally,nine examples and three sets of experiments in total were designed to verify the effectiveness and superiority of the proposed IGWO algorithm.
关 键 词:柔性作业车间分批调度问题 改进灰狼优化算法 流体模型 自适应邻域搜索
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