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

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 th...

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Vydané v:Ege akademik bakıs Ročník 23; číslo 2; s. 237 - 246
Hlavní autori: Çİçeklİ, Ural Gökay, Demİrcan Keskİn, Fatma, Kocamaz, Murat
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Izmir Ege University Faculty of Economics and Administrative Sciences 01.05.2023
Predmet:
Genetic algorithms
Heuristic
Optimization algorithms
Scheduling
ISSN:1303-099X
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Shrnutí: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 (SS), Genetic Algorithm (GA), and Greedy Randomized Adaptive Search Procedures (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 (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.
Bibliografia:ObjectType-Article-1
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ISSN:1303-099X
DOI:10.21121/eab.1246770