基于改进紧致遗传算法的柔性流水车间组批排产优化问题研究

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.

Study for the flexible flow shop scheduling problem with batch process machines based on an advanced compact genetic algorithm

In order to solve the flexible flow shop scheduling problem with batch process machines (FFSP-PBM), both the change of jobs' processing methods and how jobs will be grouped in the batching process stages are analyzed, the FFSP-BPM's mathematical model is constructed, and a self-adaptive co-evolution compact genetic algorithm (SCCGA) which contains three modifications including the individual selection strategy in terms of Hanming distance, the probabilistic model updating mechanism with two individuals and the self-adaptive elite inherence strategy over the standard compact genetic algorithm, is proposed as the global optimizing tool. Furthermore, the best parameters are set after some relative tests. Results of the controlled trial in the last show the efficiency of our proposed SCCGA in solving the FFSP-BPM in the realistic production.