Application of genetic algorithm, GA, to solve a flow shop scheduling problem with changeover times in operations: a case study

Abstract

Flow Shop Scheduling (FSS) Problems are examples of combinatorial optimization issues that are classified as NP-hard. Because of the NP-hard structure of FSS problems, it can be extremely challenging to find mathematical modeling methodologies that will result in an optimal solution for these problems. The Genetic Algorithm (GA), which is a metaheuristic approach, is one of the most important factors in the process of locating near-optimal answers to NP-hard optimization issues. In this research, a GA model for addressing an FSS problem was developed with the goal of lowering the overall weighted tardiness time and placing a constraint on the operation changeover time. When compared with the performance of the standard heuristics EDD, being used in the company under study, the GA model’s performance was shown to be superior. Based on the findings, it can be shown that the objective value was cut by 43%, going from 215.95 (h) to 123.07 (h). This demonstrates that the GA model is an effective strategy for addressing FSS problems.