Optimization of product design process in cloud manufacturing system based on swarm intelligence

• Published in: International journal of computer integrated manufacturing Vol. 38; no. 5; pp. 577 - 595
• Main Authors: Shang, Xianru, Liu, Zijian, Gong, Chen
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 04.05.2025
• Subjects: Cloud manufacturing; cross-mutated particle swarm algorithm; quality of service model
• ISSN: 0951-192X; 1362-3052
• DOI: 10.1080/0951192X.2024.2314780

Currently, manufacturing industries in many countries are grappling with an imbalance in the supply and demand of manufacturing resources, encompassing resource scarcity and idleness, as well as insufficient and surplus manufacturing capabilities. To address this issue, cloud manufacturing has emerged, integrating the Internet of Things, cloud computing, and advanced manufacturing technologies. This fusion enables the on-demand allocation of manufacturing resources and capabilities, facilitating efficient networked production. However, in the realm of the Internet, the challenge lies in efficiently aligning abundant information with a company's manufacturing objectives to genuinely deliver an "on-demand service." This represents a pivotal issue that demands immediate resolution within the operational framework of cloud manufacturing platforms. To effectively address the optimization challenge in Manufacturing Cloud Services (MCS), this study leverages two dimensions: the fundamental Quality of Service (QoS) of MCSs and the interrelated QoS among these services. A holistic QoS model for assembling MCSs is constructed. In this model, the timely relevance of fundamental QoS data is ensured through the incorporation of a temporal weighting function, while an enhancement in the precision of optimization is achieved by considering the interconnections between service combinations. The final simulation experiments showcase the effectiveness of the proposed approach in this paper.