作业车间动态调度研究

系统地研究了动态环境中受多种资源制约的多目标作业车间批量调度问题，提出了一种新调度算法。该算法采用基于周期和事件驱动的调度策略将动态调度过程分成多个连续静态调度区间，然后在各区间内采用多目标的遗传算法优化调度方案。本文讨论了再调度周期的确定方法、待加工工件的选取原则、批量生产的处理方法等。针对研究的调度问题设计了包含工序及其加工机床、工人、运输小车信息的染色体编码，并对染色体的解码方法、选择方法、交叉方法和变异方法进行了研究。仿真结果表明，该算法可行，优越性明显。

Rolling Time Horizon Job-Shop Scheduling Strategy in Dynamic Environment

The job-shop scheduling problem with multi-objective and multi-resource constrains for the batch process in dynamic manufacturing systems is investigated. Then a robust procedure is used for soloving this problem. In the procedure, periodic and event-driven rescheduling strategies are employed and the dynamic scheduling problem is decomposed into a series of static scheduling problems, and then the multi-objective genetic algorithms are adopted to optimize each of the static scheduling problems. How to decide rescheduling period, how to split a batch into small batches and how to gather new information from the current shop floor status are discussed. Representation based on operation, machine, worker and vehicle is designed. Then crossover operation, mutation operation and decoding operation for the designed representation are explored. An scheduling example show that the method is available and efficient.