Job-splitting and Batching Hybrid Flow Shop Scheduling Problem with Queue Time Constraints

In this study, we consider a job-splitting and batching hybrid multi-stage flowshop with queue time constraint. A hybrid flowshop is composed of a series of production stages with several identical parallel machines at each stage. Jobs are processed through all stages in the same production flow. In many real world applications, there are often queue time limitations among process stages. Any violation of the process queue time constraint affects yield quality and also incurs significant scrap costs. Furthermore, Lot streaming combined job splitting with operations overlapping is one of the effective techniques used to implement the time-based strategy in today’s era of global competition. Besides, Batching in a manufacturing system is very common policy in most industries. The main reasons for batching are avoidance of set ups and/or facilitation of material handling. As a consequence, we purpose a hybrid flowshop scheduling problem which combines with job splitting, batching and queue time constraint in order to solve a complex combinatorial problem encountered in many real world applications. The objective is to minimize the total completion time of all of jobs under minimizing the number of jobs violating the queue time constraint. We formulate this problem as a mixed integer linear programming model (MILP) . Computational tests have shown that the total elapsed time resulting from the purposed formulation which solves the large-scale problem is costly. Therefore, we present a solution approach, a kind of heuristic, based on the characteristic of the problem. Numerical results show that this solution approach generates higher quality solutions in moderate computational time.