Improve the Performance of a Complex FMS with a Hybrid Machine Learning Algorithm

Abstract

Modern manufacturing systems are expected to undertake multiple tasks, flexible for extensive customization, and that trends make production systems become more and more complicated. The advantage of a complex production system is a capability to fulfill more intensive goods production and to adapt to various parameters in different conditions. The disadvantage of a complex system, on the other hand, with the pace of the increase of complexity, lies in the control difficulties rising dramatically. Moreover, classical methods are reluctant to control a complex system, and searching for the appropriate control policy tends to become more complicated. Thanks to the development of machine learning technology, this problem is provided with more possibilities for the solutions. In this paper, a hybrid machine learning algorithm, integrating genetic algorithm and reinforcement learning algorithm, is proposed to cope with the accuracy of a control policy and system optimization issue in the simulation of a complex manufacturing system. The objective of this paper is to cut down the makespan and the due date in the manufacturing system. Three use cases, based on the different recipe of the product, are employed to validate the algorithm, and the results prove the applicability of the hybrid algorithm. Besides that, some additionally obtained results are beneficial to find out a solution for the complex system optimization and manufacturing system structure transformation.