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.
Highlights
Over the past few years, industrial manufacturing is confronted with extensive changes
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
Because control units on real plants are programmable logic controller (PLC)-mounted, the simulation is executed on CoDeSysV3.5 SP5 Patch 3
Summary
Over the past few years, industrial manufacturing is confronted with extensive changes. H. Li omy, markets require highly qualified and customized products at lower costs and with shorter life cycles [1]. Markets require highly qualified and customized products at lower costs and with shorter life cycles [1] To dispose of these challenges, the performance of their production system has to be improved by the manufacturing enterprises. New concepts for manufacturing system have been developed. An agile manufacturing system brings to production much greater concurrency and integration of activities [2]. A coupled cyberphysical system scheme of predictive manufacturing system is developed to integrate, manage and analyse machinery or process data to operate more efficiently during life cycle by Lee et al [4]
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