Abstract
In this research we study a hybrid flow shop scheduling with lot split and machine assignment problem, the order would be split up several sublots, and the machine assignment is that each order would be assigned to eligibility machine groups at each stage. In this research we study a three-stage hybrid flow shop production system, with several unrelated parallel machine groups at each stage, and there are some identical parallel machines at each machine group. We consider that processing time is stochastic, and each product type has its eligible machine groups. To reduce mean completion time, lot split would be used in this production system. Each order would be separated into several sublots to reduce flow time by the overlapping of operations. All in all, this research would discuss two issues, lot split and machine group assignment. Lot split is that each order would be separated into several equal size sublots, and we look for suitable quantity. Machine group assignment is that each order would be assigned to eligible machine groups at each stage. We simultaneously consider lot split and machine group assignment, so that solution space is too large. We use genetic algorithm to search for suitable solution. From the result, we find that lot split is useful to mean completion time, and variable lot split performs better than consistent lot split. In addition, we take stochastic processing time into condition, and it spends many times to reduce the sampling variability. Therefore, we use optimal computing budget allocation to improve efficiency. Computation results indicate that it reduces 76% simulation times by using OCBA.