Evolutionary game based real-time scheduling for energy-efficient distributed and flexible job shop

Abstract

With the global energy crisis and environmental issues becoming severe, more attention has been paid to production scheduling considering energy consumption than ever before. However, in the context of intelligent manufacturing, most studies apply the industrial internet of things (IIoT) to improve energy efficiency. It may cause the real-time data in the workshop unable to be collected and treated timely, thus affecting the real-time decision-making of the scheduling system. Edge computing (EC) can make full use of embedded computing capabilities of field devices to process real-time data and reduce the response time of making production decisions. Therefore, in this study, an overall architecture of the EC-IIoT based distributed and flexible job shop real-time scheduling (DFJS-RS) is proposed to enhance the real-time decision-making capability of the scheduling system. The DFJS-RS method, which consists of the task assignment method of the shop floor layer and the RS method of the flexible manufacturing units (FMUs) layer, is designed and developed. An evolutionary game-based solver method is adopted to obtain the optimal allocation. Finally, a case study is employed to validate the DFJS-RS method. The results show that compared with the existing production scheduling method, the DFJS-RS method can improve energy efficiency by up to 26%. This improvement can further promote cleaner production (CP) and sustainable societal development.