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为了解决柔性流水车间组批排产优化问题(flexible flow shop scheduling problem with batch process machines,FFSP-BPM),对组批加工环节中工件加工方式的变化以及工件的组批方式进行了分析,建立了:FFSP-BPM的数学规划模型,并在标准紧致遗传算法的基础上,加入了基于汉明距离的个体选择机制,双个体概率模型更新机制和基于进化停滞代数的自适应精英继承策略三处改进,提出一种自适应协同进化紧致遗传算法(self-adaptive co-evolut,ion compact geneticr algorithm,SCCGA)作为全局优化算法.设计仿真实验,对算法中新引入的参数进行分析和探讨,确定了最佳参数值,最后通过实例测试,并与其他算法进行对比研究,验证了本算法对于解决实际生产中:FFSP-BPM这类排产问题的有效性.
In order to solve the problem of flexible flowshop scheduling problem with batch process machines (FFSP-BPM), this paper analyzes the changes of the workpiece processing methods and the batch grouping methods in batch process, : Mathematical programming model of FFSP-BPM, and based on the standard compact genetic algorithm, an individual selection mechanism based on Hamming distance, a two-entity probability model updating mechanism and an adaptive elite inheritance strategy based on evolutionary stagnation algebra are introduced , An adaptive co-evolution compact genetic algorithm (SCCGA) is proposed as a global optimization algorithm.A simulation experiment is designed to analyze and discuss the newly introduced parameters in the algorithm to determine The best parameter value is obtained. Finally, the case test and the comparison with other algorithms are carried out to verify the effectiveness of the algorithm in solving the scheduling problem of FFSP-BPM in actual production.