An adaptive model of manufacturing service composition based on conflict detection

Jianxiong Zhang; Xuefeng Ding; Ke Du; Jun Tang; Chao Tang; Dasha Hu; Yuming Jiang

doi:10.1117/12.2647215

7 September 2022 An adaptive model of manufacturing service composition based on conflict detection

Jianxiong Zhang, Xuefeng Ding, Ke Du, Jun Tang, Chao Tang, Dasha Hu, Yuming Jiang

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123290F (2022) https://doi.org/10.1117/12.2647215
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

In cloud manufacturing (CMfg), various services with different functions are aggregated into an optimal service composition to handle complex manufacturing tasks, while maximizing service quality. However, the collaborative effect between services has a significant impact on the service composition, and the conflict between tasks may also affect the operation of the service composition in a real manufacturing environment. To handle these problems, this paper proposes an adaptive model of manufacturing service composition based on conflict detection. First, the preference information of both service parties and the collaborative performance between the services are considered to obtain a more realistic and satisfactory service composition. Subsequently, resource conflicts are detected in real time, and resolved according to their types, so as to realize the self-adaptation of the service composition. Finally, the illustrative case and experimental results are conducted to verify the feasibility and effectiveness of the proposed model.

Citation Download Citation

Jianxiong Zhang, Xuefeng Ding, Ke Du, Jun Tang, Chao Tang, Dasha Hu, and Yuming Jiang "An adaptive model of manufacturing service composition based on conflict detection", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123290F (7 September 2022); https://doi.org/10.1117/12.2647215

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