基于GASA优化算法的不确定条件下Job-Shop调度问题研究

Job-Shop scheduling under uncertainty based on genetic algorithm simulated annealing

针对不确定因素和动态干扰事件下Job-Shop调度问题,基于模糊数理论和动态调度策略,综合考虑完工时间、机器加工成本和机器负荷,建立了作业车间多目标不确定性调度模型;为了求解该调度模型,结合遗传算法和模拟退火算法的特点,设计了遗传模拟退火混合算法,并针对作业车间的复杂性要求,对算法的编码解码、交叉变异算子以及保优策略等方面进行了改进;通过仿真,得到了初始调度方案,然后在此基础上,采用动态调度策略对紧急插单、机器故障、工件取消和交货期变更等不确定干扰事件进行了研究。通过对某电动产品生产公司车间进行的实证研究结果表明,根据上述研究得到的较好的调度方案,可以有效地提高机器利用率和客户满意度。该模型和算法能够较好地应用到企业实际生产中。

Aiming at the Job-Shop scheduling problem under uncertainties and dynamic disruption events,a new Job-Shop scheduling model was set up,which based on fuzzy number theory and dynamic scheduling strategy and considering of the makespan,manufacturing cost of machines and machine load.To get the optimization,genetic simulated annealing algorithm was designed and improved.The initial scheduling scheme was gotten through simulation,and on this basis,uncertain disturbance events was studied by dynamic scheduling strategy.Empirical researching of production workshop in an electric products company was done.The better scheduling soheme was gotten,the machine utilization and customer satisfaction was effectively improved.The results indicate that the model and algorithm are feasibility and can be well used into real workshop.