A cloud manufacturing service composition optimization method for fuzzy demands based on improved NSGA-III algorithm

IF 11.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing Pub Date : 2025-08-20 DOI:10.1016/j.rcim.2025.103106

Xianhui Liu , Run Yang , Xiaobin Li , Xi Vincent Wang

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引用次数: 0

Abstract

The Industrial Internet integrates industrial systems with advanced Internet technologies to establish an intelligent implementation platform and diversified service ecosystem for cloud manufacturing. However, the extensive user base introduces substantial uncertainties in temporal, financial, and operational requirements of cloud manufacturing tasks. While existing studies propose various solutions, their reliance on subjective criteria for demand variation analysis leads to inadequate handling of fuzzy demands. A novel fuzzy demand-oriented optimization method is proposed for cloud manufacturing service composition, employing fuzzy sets and membership functions to establish an objective quantification framework for modeling uncertain demands. The approach formulates a multi-objective optimization model incorporating four key metrics: service cost, service time, service quality, and resource utilization rate, with fuzzy satisfaction functions constructing constraints containing random variables to ensure robust realization of fuzzy demands. An enhanced NSGA-III algorithm is developed featuring opposition-based learning mechanisms and GKM-based reference point generation to enhance population diversity and convergence efficiency. Validation through benchmark functions and practical cloud manufacturing scenarios confirms the method’s effectiveness in addressing fuzzy demand challenges, with the co-evolution algorithm demonstrating superior convergence and diversity performance.

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基于改进NSGA-III算法的模糊需求云制造服务组合优化方法

工业互联网将工业系统与先进的互联网技术相融合，为云制造构建智能实施平台和多元化服务生态系统。然而，广泛的用户基础给云制造任务的时间、财务和操作需求带来了巨大的不确定性。虽然现有的研究提出了各种解决方案，但它们对需求变化分析的主观标准的依赖导致对模糊需求的处理不足。提出了一种面向云制造服务组合的模糊需求优化方法，利用模糊集和隶属函数建立了不确定需求建模的客观量化框架。该方法建立了包含服务成本、服务时间、服务质量和资源利用率四个关键指标的多目标优化模型，并利用模糊满足函数构造包含随机变量的约束，保证模糊需求的鲁棒性实现。为了提高种群多样性和收敛效率，提出了一种基于对立学习机制和基于gkm参考点生成的改进NSGA-III算法。通过基准函数和实际云制造场景的验证证实了该方法在解决模糊需求挑战方面的有效性，协同进化算法表现出卓越的收敛性和多样性性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Robotics and Computer-integrated Manufacturing 工程技术-工程：制造

CiteScore

24.10

自引率

13.50%

发文量

160

审稿时长

50 days

期刊介绍： The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.