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A skill vector-based multi-task optimization algorithm for achieving objectives of multiple users in cloud manufacturing
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AI
DOI：10.1016/j.aei.2025.103295.png)
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Abstract
En 中文
Cloud Manufacturing (CMfg) is a new manufacturing mode that provides efficient manufacturing services to customers by centrally scheduling manufacturing resources distributed across various regions. In CMfg, each participant is an independent economic entity with distinct objectives and effectively achieving the objectives of customers, suppliers, and the CMfg platform under limited resources is a significant challenge. To solve this problem, this study first proposed a three-level multi-task optimization (TMTO) model. The upper-level and lower-level of the TMTO model respectively optimize the personalized objectives of customers and suppliers, as well as the objectives of the CMfg platform are optimized at the middle-level. Subsequently, a skill vector-guided multi-task optimization algorithm (SMTOA) is proposed to collaboratively optimize the objectives of all participants, with the skill vector designed to evaluate the ability of scheduling schemes to meet the objectives of all customers and suppliers. Finally, experimental cases based on an aerospace manufacturing enterprise confirm the effectiveness of the TMTO model and the advantages of SMTOA in solving the TMTO model.
Keywords:
Cloud manufacturing
Service composition optimization
Multi-task optimization
Evolutionary algorithm
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