A statistical comparison of metaheuristics for unrelated parallel machine scheduling problems with setup times

Manufacturing scheduling aims to optimize one or more performance measures by allocating a set of resources to a set of jobs or tasks over a given period of time. It is an area that considers a very important decision-making process for manufacturing and production systems. In this paper, the unrelated parallel machine scheduling problem with machine-dependent and job-sequence-dependent setup times is addressed. This problem involves the scheduling of tasks on unrelated machines with setup times in order to minimize the makespan. The genetic algorithm is used to solve small and large instances of this problem when processing and setup times are balanced (Balanced problems), when processing times are dominant (Dominant P problems), and when setup times are dominant (Dominant S problems). For small instances, most of the values achieved the optimal makespan value, and, when compared to the metaheuristic ant colony optimization (ACOII) algorithm referred to in the literature, it was found that there were no significant differences between the two methods. However, in terms of large instances, there were significant differences between the optimal makespan obtained by the two methods, revealing overall better performance by the genetic algorithm for Dominant S and Dominant P problems.FCT—Fundação para a Ciência e Tecnologia through the R&D Units Project Scope UIDB/00319/2020 and EXPL/EME-SIS/1224/2021 and PhD grant UI/BD/150936/2021

Multi-objective optimization of the job shop scheduling problem on unrelated parallel machines with sequence-dependent setup times

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.USDA - U.S. Department of Agriculture(PCIF/GRF/0141/2019)This work has been supported by FCT – Funda¸c˜ao para a Ciˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/202