Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods

JOURNAL OF CLEANER PRODUCTION

卷:247

Article

Wang, Jin^1,2  Yang, Jiahao¹  Zhang, Yingfeng^1,3  Ren, Shan^1,2  Liu, Yang^4,5 

[1] Northwestern Polytech Univ, Minist Ind & Informat Technol, Key Lab Ind Engn & Intelligent Mfg, Xian 710072, Shaanxi, Peoples R China

[2] Xian Univ Posts & Telecommun, Sch Modern Post, Xian 710061, Shaanxi, Peoples R China

[3] Shaanxi Univ Technol, Sch Mech Engn, Xian 723001, Shaanxi, Peoples R China

[4] Linkoping Univ, Dept Management & Engn, SE-58183 Linkoping, Sweden

[5] Univ Vaasa, Dept Prod, Vaasa 65200, Finland

关键词: Energy consumption;    Real-time scheduling;    Flexible job shop;    Infinitely repeated game;   

DOI  :  10.1016/j.jclepro.2019.119093

来源: Elsevier

【 摘 要 】

Production scheduling has great significance for optimizing tasks distribution, reducing energy consumption and mitigating environmental degradation. Currently, the research of production scheduling considering energy consumption mainly focuses on the traditional manufacturing workshop. With the wide application of the Internet of Things (IoT) technology, the real-time data of manufacturing resources and production processes can be retrieved easily. These manufacturing data can provide opportunities for manufacturing enterprises to reduce energy consumption and enhance production efficiency. To achieve these targets, a multi-period production planning based real-time scheduling (MPPRS) approach for the loT-enabled low-carbon flexible job shop (LFJS) is presented in this study to carry out real-time scheduling based on the real-time manufacturing data. Then, the mathematical models of real-time scheduling are established to achieve production efficiency improvement and energy consumption reduction. To obtain a feasible solution, an infinitely repeated game optimization approach is used. Finally, a case study is implemented to analyse and discuss the effectiveness of the proposed method. The results show that in general, the proposed method can achieve better results than the existing dynamic scheduling methods. (C) 2019 Elsevier Ltd. All rights reserved.

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