An MO‐GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem

Abstract

This paper addresses the multiobjective hybrid flow shop (MOHFS) scheduling problem. In the MOHFS problem considered here, we have a set of jobs that must be performed in a set of stages. At each stage, we have a set of unrelated parallel machines. Some jobs may skip stages. The evaluation criteria are the minimizations of makespan, the weighted sum of the tardiness, and the weighted sum of the earliness. For solving it, an algorithm based on the multiobjective general variable neighborhood search (MO‐GVNS) metaheuristic, named adapted MO‐GVNS, is proposed. This work also presents and compares the results obtained by the adapted MO‐GVNS with those of four other algorithms: multiobjective reduced variable neighborhood search, nondominated sorting genetic algorithm II (NSGA‐II), and NSGA‐III, and another MO‐GVNS from the literature. The results were evaluated based on the Hypervolume, Epsilon, and Spacing metrics, and statistically validated by the Levene test and confidence interval charts. The results showed the efficiency of the proposed algorithm for solving the MOHFS problem.

Short‐term scheduling with machine calibration

“…Of course, it would be of great interest to study the calibration engineer scheduling problem in distributed multiple‐machine settings where several calibration engineers are available. It is also worth considering the coordination of machine calibrations in some stages or machines of a flow shop (Ferone et al., 2020; de Siqueira et al., 2020; Hakeem‐Ur‐Rehman et al., 2021), a job shop (Benttaleb et al., 2019; Abedi et al., 2020; Ham, 2020), or a parallel machine setting where a server is in charge of setups (Arnaout, 2017; Bektur and Saraç, 2019; Krim et al., 2020) such that the production runs smoothly from an overall perspective.…”

Scheduling with day shifts and breaks

“…Further research may focus on problems with some other criteria such as minimizing total completion time (Parsa et al., 2019; Zhu et al., 2019) and maximizing the noon break period under a given deadline for all the jobs or problems in some other production environments such as flow shop (Hakeem‐Ur‐Rehman et al., 2019; Krim et al., 2019b; Ferone et al., 2020; de Siqueira et al., 2020), job shop (Benttaleb et al., 2018, 2019; Kubiak et al., 2020), and parallel machine with a single server (Arnaout, 2017; Bektur and Saraç, 2019). It is also worth considering scheduling problems in industries that are open around the clock, such as in airports, restaurants, hospitals, movie theaters, and in some retail stores, where morning, afternoon, and night shifts are involved.…”

Energy-efficient distributed heterogeneous re-entrant hybrid flow shop scheduling problem with sequence dependent setup times considering factory eligibility constraints

“…Since there are few studies on the DHRHFSP-SDST, three representative algorithms NSGA-II 26 , MO-GVNS 27 and SPEA-II are selected for comparative study. These three algorithms are widely used in solving mixed flow shop scheduling problems.…”