A Q-LEARNING BASED SELECTIVE DISASSEMBLY PLANNING SERVICE IN THE CLOUD BASED REMANUFACTURING SYSTEM FOR WEEE

摘要

Cloud based approach for remanufacturing is becoming a new technical solution for sustainable management of Waste Electrical and Electronic Equipment (WEEE). This paper presents a service-oriented framework of a Cloud Based Remanufacturing System (CBRS) for WEEE. In remanufacturing of WEEE, disassembly plays an important role. However, complete disassembly is rarely an ideal solution due to the high disassembly cost, with the increasing customization and diversity, and more complex assembly processes of Electrical and Electronic Equipment (EEE). Selective disassembly focusing on disassembling only a few selected components is a better choice. In this paper, a Q-Learning based Selective Disassembly Planning (QL-SDP) approach embedded with a multi-criteria decision making model is developed. The multi-criteria decision making model is built according to the legislative and economic considerations of specific stakeholders of WEEE. And the QL-SDP approach is used to achieve optimized selective disassembly planning. An implementation example has been used to verify and demonstrate the effectiveness and robustness of the approach. The developed QL-SDP approach is designed as a service implemented in the presented CBRS for WEEE.