The Multiple Objectives Flexible Jobshop Scheduling Using Reinforcement Learning

Jobshop Scheduling Problem is a classic complex problem in every field, such as education, business, and daily life. This problem has been changed due to the changing of problem space. For this reason, JSP problems are categorized into many different types, which consist of The General Jobshop Scheduling (GJSP), The Flexible Jobshop Scheduling (FJSP) and The Multiple-routes Jobshop Scheduling (MrJSP). However, most of the research that tries to solve the JSP problem has focused on the shortest makespan scheduling. Still, sometimes the minimum makespan can be led to very high operating costs, which have a significant impact on operating results. Therefore, the Multiple-objectives Flexible Jobshop Scheduling Problem (M-FJSP) become the focused problem in this research. The proposed method is a Reinforcement Learning Model (RL) with a Q-Learning algorithm. The experimental dataset uses data from the OR-Library, which is the collection for a variety of Operation Research (OR) problems. Our proposed models will be compared between the three different states definition in which we expect the metaheuristic model will be the best performance model.