Abstract
Several optimization criteria are involved in the job shop scheduling problems encountered in the engineering area. Multi-objective optimization algorithms are often applied to solve these problems, which become even more complex with the advent of Industry 4.0, mostly due to the increase of data from industrial systems. In this work, several instances of the multi-objective job shop scheduling problem on unrelated parallel machines with sequence-dependent setup times are solved using evolutionary approaches. In this problem, the goal is to assign a set of N jobs on M unrelated machines considering sequence-dependent setup times. Several objectives such as makespan, average completion time, cost and energy consumption can be optimized. In this work, single and multi-objective optimization problems are solved considering the minimization of makespan and the average completion time. Preliminary results for the comparison of algorithms on different instances of the problems are presented and statistically analysed. Future work will include problems with more objectives, and to extend this approach to the distributed job shop problem.
This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020.
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dos Santos, F., Costa, L., Varela, L. (2023). Multi-objective Optimization of the Job Shop Scheduling Problem on Unrelated Parallel Machines with Sequence-Dependent Setup Times. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14105. Springer, Cham. https://doi.org/10.1007/978-3-031-37108-0_32
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