Application of Meta-heuristic Algorithm to Solve a Flow Shop Scheduling Optimization Problem: A Case Study

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), a meta-heuristics approach, is one of the most important factors in locating near-optimal answers to NP-hard optimization issues. In this research, a GA model for addressing an FSS problem is developed to lower the overall weighted tardiness time and constrain the operation changeover time. When compared with the standard heuristics EDD, being used in the company under study, the GA model’s performance was 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.