APPLICATION OF A MACHINE LEARNING ALGORITHM IN A MULTI STAGE PRODUCTION SYSTEM

Abstract

This paper examines a permutation flow-shop scheduling problem, which is a complex combinatorial problem encountered in many practical applications. The objective of the research is to reduce the maximum completion time, i.e., the makespan of all jobs. In order to increase productivity and to meet the demand, manufacturers are continuously under pressure to attain the shortest possible completion time. Estimation of accurate cycle time can tremendously help production planning and scheduling in manufacturing industries. Since production planning is characterised by NP-hardness and a wide range, traditional optimization methods and heuristic rules are unable to find satisfactory solutions. Q-learning, a type of reinforcement learning algorithm, is used in this paper to find a solution that is close to being optimal. Q-learning is a branch of machine learning referring to the way an intelligent agent should act in order to maximize the concept of cumulative reward in a given environment. To validate the performance of the algorithm, Taillard's benchmark problems were solved and compared with the upper bound value. The results showed that the performance of the algorithm is better and has low computational time. Based on the performance of the proposed algorithm, two case studies were done and the solutions are compared with the performance of a metaheuristic algorithm. The result shows that the proposed algorithm can effectively and efficiently solve the problem stated above and that it is an interesting solution to resolving complex scheduling problems.