A Hybrid Metaheuristic Approach for Minimizing the Total Flow Time in A Flow Shop Sequence Dependent Group Scheduling Problem

Antonio Costa, Fulvio Cappadonna, Sergio Fichera
2014 Algorithms  
Production processes in Cellular Manufacturing Systems (CMS) often involve groups of parts sharing the same technological requirements in terms of tooling and setup. The issue of scheduling such parts through a flow-shop production layout is known as the Flow-Shop Group Scheduling (FSGS) problem or, whether setup times are sequence-dependent, the Flow-Shop Sequence-Dependent Group Scheduling (FSDGS) problem. This paper addresses the FSDGS issue, proposing a hybrid metaheuristic procedure
more » ... ic procedure integrating features from Genetic Algorithms (GAs) and Biased Random Sampling (BRS) search techniques with the aim of minimizing the total flow time, i.e., the sum of completion times of all jobs. A well-known benchmark of test cases, entailing problems with two, three, and six machines, is employed for both tuning the relevant parameters of the developed procedure and assessing its performances against two metaheuristic algorithms recently presented by literature. The obtained results and a properly arranged ANOVA analysis highlight the superiority of the proposed approach in tackling the scheduling problem under investigation. Setup times of machine Mi 1 M 1 → U [1,50] M 2 → U [17,67] M 3 → U [45,95] 2 M 1 → U [17,67] M 2 → U [17,67] M 3 → U [17,67] 3 M 1 → U [45,95] M 2 → U [17,67] M 3 → U [1,50] 4 M 1 → U [1,50] M 2 → U [17,67] M 3 → U [17,67] 5 M 1 → U [1,50] M 2 → U [17,67] M 3 → U [1,50] 6 M 1 → U [17,67] M 2 → U [17,67] M 3 → U [45,95] 7 M 1 → U [17,67] M 2 → U [17,67] M 3 → U [1,50] 8 M 1 → U [45,95] M 2 → U [17,67] M 3 → U [45,95] 9 M 1 → U [45,95] M 2 → U [17,67] M 3 → U [17,67]
doi:10.3390/a7030376 fatcat:2fr33bof6zhsbjmj7etujq3jsi