Minimizing Makespan in a Permutation Flow Shop Environment: Comparison of Scatter Search, Genetic Algorithm and Greedy Randomized Adaptive Search Procedures

Authors : Ural Gökay ÇİÇEKLİ, Fatma DEMİRCAN KESKİN, Murat KOCAMAZ

Pages : 237-246

Doi:10.21121/eab.1246770

View : 91 | Download : 13

Publication Date : 2023-05-10

Article Type : Research Paper

Abstract :Solving scheduling problems enables more efficient use of production capacity. It involves defining the sequence of operations, determining the capacity of resources, and balancing workloads. Different methods, especially metaheuristics, have been used to solve these problems. This study presents the application of Scatter Search insert ignore into journalissuearticles values(SS);, Genetic Algorithm insert ignore into journalissuearticles values(GA);, and Greedy Randomized Adaptive Search Procedures insert ignore into journalissuearticles values(GRASP); for minimizing makespan in a permutation flow shop environment. In this study, the performances of these methods are compared through various test problems in the literature and a real-life problem of a company operating in the automotive sector. Study comprises 48 jobs that must be planned within a day for eight consecutive operations. In cellular manufacturing, the sequence in which each job is processed in eight operations is the same. In solving permutation flow shop scheduling problems insert ignore into journalissuearticles values(PFSP);, one of the NP-hard problems, meta-heuristic methods are widely applied due to their successful results. From this point of view, SS, GA, and GRASP are employed in this study, and their performances are compared.
Keywords : Scheduling, Permutation Flow Shop Scheduling Problem, Makespan, Scatter Search, Genetic Algorithm, GRASP

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