Abstract
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.
| Original language | English |
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| Title of host publication | 28th International Conference on Automated Planning and Scheduling, ICAPS 2018 |
| Pages | 298-302 |
| 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
| Conference | 28th International Conference on Automated Planning and Scheduling, ICAPS 2018 |
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| Country/Territory | Netherlands |
| City | Delft |
| Period | 24/06/18 → 29/06/18 |