@article{Adan_Adan_Akcay_Van den Dobbelsteen_Stokkermans_2018, title={A Hybrid Genetic Algorithm for Parallel Machine Scheduling at Semiconductor Back-End Production}, volume={28}, url={https://ojs.aaai.org/index.php/ICAPS/article/view/13913}, DOI={10.1609/icaps.v28i1.13913}, abstractNote={ <p> 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. </p> }, number={1}, journal={Proceedings of the International Conference on Automated Planning and Scheduling}, author={Adan, Jelle and Adan, Ivo and Akcay, Alp and Van den Dobbelsteen, Rick and Stokkermans, Joep}, year={2018}, month={Jun.}, pages={298-302} }