Augmented Lagrangian coordination for energy-optimal allocation of smart manufacturing services

• Main Authors: Zhang, Geng, Wang, Gang, Chen, Chun-Hsien, Cao, Xiangang, Zhang, Yingfeng, Zheng, Pai
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Mechanical engineering; Engineering::Electrical and electronic engineering; Energy Consumption; Augmented Lagrangian Coordination
• Online Access: https://hdl.handle.net/10356/160493
• Institution: Nanyang Technological University

The rapid development of information and communication technologies has triggered the proposition and implementation of smart manufacturing paradigms. In this regard, efficient allocation of smart manufacturing services (SMSs) can provide a sustainable manner for promoting cleaner production. Currently, centralized optimization methods have been widely used to complete the optimal allocation of SMSs. However, personalized manufacturing tasks usually belong to diverse production domains. The centralized optimization methods could hardly include related production knowledge of all manufacturing tasks in an individual decision model. Consequently, it is difficult to provide satisfactory SMSs for meeting customer's requirements. In addition, energy consumption is rarely considered in the SMS allocation process which is unfavorable for performing sustainable manufacturing. To address these challenges, augmented Lagrangian coordination (ALC), a novel distributed optimization method is proposed to deal with the energy-optimal SMS allocation problem in this paper. The energy-optimal SMS allocation model is constructed and decomposed into several loose-coupled and distributed elements. Two variants of the ALC method are implemented to formulate the proposed problem and obtain final SMS allocation results. A case study is employed to verify the superiority of the proposed method in dealing with energy-optimal SMS allocation problems by comparing with the centralized optimization method at last.