Employing Genetic Algorithm and Discrete Event Simulation for Flexible Job-Shop Scheduling Problem

In this paper, a comparative study between Genetic Algorithm and Discrete Event Simulation to solve the flexible job-shop scheduling problem is presented. Two different approaches are used to generate a flexible job-shop schedule for a pharmaceutical factory X with minimum make-span which is defined as the duration required to complete all jobs. The first approach uses Genetic Algorithm to find an optimal or near-optimal solution for the flexible job-shop problem. The second approach uses Discrete Event Simulation and predefined dispatching rules to solve the flexible job-shop problem by creating a model for the pharmaceutical factory X production line. The same case study is used to evaluate the two approaches results. The Genetic Algorithm approach showed better performance compared to the Discrete Event Simulation approach for the same case study while using different dispatching rules. Both approaches showed better performance compared to basic sequential schedule.