Social learning in self-organizing manufacturing networks based on opinion dynamics

Zhenzhong Yao; Cheng Qian; Yingfeng Zhang

doi:10.1117/12.3039260

5 August 2024 Social learning in self-organizing manufacturing networks based on opinion dynamics

Zhenzhong Yao, Cheng Qian, Yingfeng Zhang

Proceedings Volume 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024); 132263D (2024) https://doi.org/10.1117/12.3039260
Event: 3rd International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 2024, Changsha, China

Abstract

The accelerated technological advancements in cyber-physical systems and edge computing have led to the maturity of self-organizing manufacturing systems. Many studies have addressed the optimization problem throughout the resource configuration process. With the current rapid growth in the number of manufacturing resources and the complexity of the relations among resources, it is necessary to analyze the utilization and workload of resources from a large-scale network perspective, in addition to the traditional optimization metrics such as time, cost, and quality. In this paper, a social learning framework was proposed based on the opinion dynamics models. Therefore, manufacturing resources can proactively share their states, e.g., busyness level, and negotiate their respective prices for use accordingly. The dynamic pricing mechanism was designed for better workload balancing as well as production pace management. Numerical experiments showed that the proposed methods can be easily integrated with other resource configuration algorithms to further optimize workload balancing and pace management.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhenzhong Yao, Cheng Qian, and Yingfeng Zhang "Social learning in self-organizing manufacturing networks based on opinion dynamics", Proc. SPIE 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 132263D (5 August 2024); https://doi.org/10.1117/12.3039260

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