【论文信息】

Yang T, Jiang F, Fan J,Su J F,Chen W. An efficient service composition optimization method for cloud manufacturing based on an IPSO-VIKOR hybrid method[J]. Computers & Industrial Engineering, 2026, 214: 111831. https://doi.org/10.1016/j.cie.2026.111831.

【作者信息】

第一作者：杨涛，工学博士、教授、硕士生导师，研究方向：协同创新管理、多属性决策理论与方法、商业模式创新

第二作者：蒋芳，2023级企业管理硕士研究生，研究方向：多属性决策理论与方法、网络协同制造

【论文摘要】

Cloud manufacturing (CMfg) has emerged as a transformative paradigm facilitating the service-oriented transition of manufacturing enterprises. A pivotal challenge within CMfg is service composition and optimal selection (SCOS)—the process of identifying the best combination of services from distributed virtual resource pools to meet customized requirements. However, existing SCOS evaluation frameworks often lack systematic indicators for green production, consequently failing to align with increasingly stringent environmental regulations and hindering sustainable development. To bridge this gap, this paper introduces a comprehensive three-dimensional evaluation system that integrates environment, service performance, and service collaboration dimensions, employing a game-theoretic approach to assign comprehensive weights. Subsequently, a service composition and optimal selection model incorporating corporate green manufacturing (SCOS-CGM) is proposed. To address the SCOS-CGM model, we develop a hybrid approach combining an Improved Particle Swarm Optimization Algorithm with the VIKOR method (IPSO-VIKOR). Finally, a case study involving electric drive systems for new energy vehicles validates the practical applicability and effectiveness of the proposed model and method.