Multiprocessor Flow Shop Scheduling Problem with Common due Window

The objective of scheduling is to maximize capacity utilization, minimize work-in-process inventory and ensure timely delivery. The due windows problem proposes that jobs should only be finished within the time interval that meets customer needs. This research applies integer programming (IP) and ant colony optimization (ACO) to solve due window problems in a flow shop with multiprocessors (FSMP). To improve the performance of jobs within due windows, this research splits them into different numbers of processing lots so they can be processed on more than one machine simultaneously. This shortens the total weighted earliness and tardiness of the jobs. The ACO is applied as a heuristic tool for solving the scheduling problems, and the solution results show that ACO has good validity, robustness and effectiveness.