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 operations, determining capacity resources, and balancing workloads. Different methods, especially metaheuristics, have been used to solve these problems. This study presents application Scatter Search (SS), Genetic Algorithm (GA), Greedy Randomized Adaptive Procedures (GRASP) for minimizing makespan in a permutation flow shop environment. In this study, performances methods are compared through various test literature real-life problem company operating automotive sector. Study comprises 48 jobs that must be planned within day eight consecutive operations. cellular manufacturing, which each job is processed operations same. solving (PFSP), one NP-hard problems, meta-heuristic widely applied due their successful results. From point view, SS, GA, GRASP employed compared.