A hybrid genetic algorithm for parallel machine scheduling at semiconductor back-end production

J. Adan, A. Akcay, J. Stokkermans, R. van den Dobbelsteen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
9 Downloads (Pure)

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 languageEnglish
Title of host publication28th International Conference on Automated Planning and Scheduling, ICAPS 2018
Pages298-302
Number of pages5
Publication statusPublished - 1 Jan 2018
Event28th International Conference on Automated Planning and Scheduling, ICAPS 2018 - Delft, Netherlands
Duration: 24 Jun 201829 Jun 2018

Conference

Conference28th International Conference on Automated Planning and Scheduling, ICAPS 2018
CountryNetherlands
CityDelft
Period24/06/1829/06/18

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