A dynamic distributed edge-cloud manufacturing with improved ADMM algorithms for mass personalization production - Chen Dong; JiHai Luo; Qiyu Hong; Zhenyi Chen; Yuzhong Chen (Journal of King Saud University: Computer and Information Sciences, 2023)
The primary feature of Industry 4.0 is MPP (mass personalization production), which requires that consumers’ individual requests are met in large-scale production. Under MPP, there is a multitude of subtasks decomposed from production tasks that are derived from individualized requests, and allocating these subtasks properly brings high economic benefits. However, existing approaches to achieve MPP, such as cloud manufacturing and social manufacturing, generally can not provide customers with deep participation in the entire production cycle, or respond to consumers’ modification needs by a triggered mechanism. Besides, some methods are of centralized architecture, which is vulnerable to single point error and with large cloud load that is not conducive to quickly responding to consumers’ dynamic demand changes. Therefore, this paper proposes a dynamic edge-cloud manufacturing mode for MPP, which can make subtask allocation with high economic benefit through distributed computing and implementing modifications of alternating direction method of multiplier (ADMM) algorithm. Also, it proposes an original improved ADMM algorithm, named Relaxation-Based ADMM algorithm, to increase the optimization speed in large-scale cases. The experimental results show that the proposed method generally obtains a superior solution under a certain iteration count.