Abstract: Aiming at problem that the equipment resources in the workshop are affected by remaining tasks of last scheduling period,considering job batch and process path flexibility,the production scheduling optimization model of flexible job-shop with dynamic resource and job batch-size constraints was constructed.A Two-Layer Nested Genetic Algorithm (TNGA) was proposed.The outer layer genetic algorithm realized the job batch division and the process paths determination of each sub-batch,and a decoding operator based on equipment optimization method was designed to determine the process path according to the individual.The inner layer genetic algorithm was used to solve the scheduling problem under the batch and process path constraints of the outer layer genetic algorithm.The make span and job's delay time of scheduling would be fed back to the outer layer genetic algorithm to evaluate the performance of current batch and process path scheme.The batch,delivery date and equipment resource constraints were added to the flexible scheduling standard benchmarks 10*10 case to test the proposed model and algorithm.The case study demonstrated that TNGA had good comprehensive scheduling performance in optimizing the scheduling problem while rationally devising the job batch and determining the process path.

Key words: dynamic resource, flexible scheduling, batch scheduling, two-layer nested genetic algorithm

摘要： 针对实际车间生产调度过程中车间设备资源受上一调度周期剩余任务影响、工件批量和加工路径柔性等问题,构建考虑设备动态负荷和工件批量约束的柔性车间生产调度模型。提出双层嵌套式遗传算法：外层遗传算法确定工件批量划分和各子批零件的工艺路径,并设计一种基于设备优选法的解码算子来确定个体对应的批量划分中各子批零件的工艺路径;内层遗传算法确定外层遗传算法个体所对应的工件分批和加工路径约束下的调度方案,调度方案的完工时间和超出交货期时间将反馈到外层遗传算法中,用于综合评估当前分批和加工路径方案的性能,实现综合优化。最后在柔性调度标准案例10×10案例基础上增加批量、交货期、设备资源等约束,对所提模型和算法进行测试,结果表明所提算法在对工件进行合理分批和工艺路径确定的同时,能够优化调度结果,满足产品交货期和设备资源约束。

关键词: 动态资源, 柔性调度, 批量调度, 双层嵌套式遗传算法