This paper addresses batch scheduling at a back-end semiconductor plant of Nexperia. This complex manufacturing environment is characterized by a large product and batch size variety, numerous parallel machines with large capacity differences, sequence and machine dependent setup times and machine eligibility constraints. A hybrid genetic algorithm is proposed to improve the scheduling process, the main features of which are a local search enhanced crossover mechanism, two additional fast local search procedures and a user-controlled multi-objective fitness function. Testing with real-life production data shows that this multi-objective approach can strike the desired balance between production time, setup time and tardiness, yielding high-quality practically feasible production schedules.
|Title of host publication||28th International Conference on Automated Planning and Scheduling, ICAPS 2018|
|Number of pages||5|
|Publication status||Published - 1 Jan 2018|
|Event||28th International Conference on Automated Planning and Scheduling, ICAPS 2018 - Delft, Netherlands|
Duration: 24 Jun 2018 → 29 Jun 2018
|Conference||28th International Conference on Automated Planning and Scheduling, ICAPS 2018|
|Period||24/06/18 → 29/06/18|