Article
Computer Science, Artificial Intelligence
Jingchen Cong, Chun-Hsien Chen, Xuan Meng, Zhongxia Xiang, Liang Dong
Summary: As an emerging IT-driven business paradigm, smart product-service system (Smart PSS) offers both smart, connected products and generated services, making it a significant research topic. This study proposes a conceptual design method for Smart PSS by analyzing user-generated emotions/feelings. Traditional products are identified, and their public review data is used to analyze user emotions/feelings. An interactive emotion board is introduced as a design tool to organize user-generated emotions/feelings and potential design points. The AHP is utilized for evaluating the improved solution.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Engineering, Industrial
Minghai Yuan, Xianxian Cai, Zhuo Zhou, Chao Sun, Wenbin Gu, Jinting Huang
Summary: This paper aims to study the scheduling method of dynamic service resources in cloud manufacturing environment. By improving the ant optimisation algorithm and utilizing functions in the genetic algorithm, a genetic-ant optimisation fusion algorithm is proposed to solve the optimal scheduling model established in this study. The proposed model and algorithm are proven to be more feasible and effective compared to traditional genetic algorithm and ant optimisation algorithm through a case study on car component production.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Hai Zhu, Wenan Tan, Mei Yang, Kai Guo, Jiaojiao Li
Summary: Research on service clustering for manufacturing network by deep learning is an effective approach for service discovery and management in manufacturing industries. This study proposes a deep manufacturing cloud service clustering model using pseudo-labels, which combines graph topology and node features to cluster nodes with similar attributes. Experimental results show that this method outperforms current advanced deep clustering methods on public and simulated datasets.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Yunliang Huo, Ji Xiong, Qianbing You, Zhixing Guo, Hai Xiang
Summary: This paper proposed a cloud personalized method based on the Evolutionary Game Algorithm to provide consumers with personalized cloud manufacturing services. An optimization model was established to achieve personalized service customization, and experiments showed that the method has much greater efficiency than existing evolutionary methods.
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2021)
Article
Computer Science, Interdisciplinary Applications
Weiqing Xiong, Ming K. Lim, Ming-Lang Tseng, Chao Wang
Summary: In this study, a comprehensive trust evaluation system is constructed in the cloud manufacturing platform. A manufacturing services trust evaluation and preprocessing model is proposed, and an improved algorithm is developed to find the Pareto-optimal solutions. The effectiveness of the proposed model and algorithm is demonstrated through case studies.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Minglun Ren, Liangjia Shao
Summary: This paper proposes a mechanism of Service Takt for services collaboration in order to address the issues of uncertainty and disturbances. By analyzing the typical characteristics of service-oriented manufacturing, the attributes and calculating processes of Service Takt are elaborated, and validated through application cases.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Thermodynamics
Dingding Hu, Kaile Zhou, Fangyi Li, Dawei Ma
Summary: With the rapid development of electric vehicles, understanding different types of EV users is crucial for business innovation in the EV sector. This study proposes an integrated approach using data mining and clustering analysis to classify EV users into six groups and provides marketing strategies to improve user loyalty and profitability for charging service enterprises.
Article
Chemistry, Analytical
Cheng-lei Zhang, Jia-jia Liu, Hu Han, Xiao-jie Wang, Bo Yuan, Shen-le Zhuang, Kang Yang
Summary: This study proposes a task-service network node matching method based on a multi-objective optimization model to solve the resource allocation issues in Cloud 3D printing, aiming to reduce manufacturing and service costs while considering the interests of enterprises and cloud platform operators. By modifying mathematical algorithms, the feasibility, effectiveness, and stability of the model and algorithm are demonstrated through solving model examples.
Article
Computer Science, Interdisciplinary Applications
Huagang Tong, Jianjun Zhu
Summary: In order to address sharing challenges in large-scale manufacturing platforms, a two-stage method is proposed. The first stage utilizes cloud models and a density-based method to address efficiency and sharing difficulties. The second stage establishes game models and designs an enhanced grey wolf algorithm for stable matching.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Automation & Control Systems
Chunhe Song, Haiyang Zheng, Guangjie Han, Peng Zeng, Li Liu
Summary: Service uncertainty modeling is an important problem in the optimization of manufacturing service composition. This article presents a cloud manufacturing service composition optimization framework based on cloud-edge collaboration that takes into account manufacturing service uncertainty. The framework proposes a method for estimating the uncertainty of manufacturing services based on Gaussian mixture regression on the edge side, and adopts an intelligent evolutionary algorithm on the cloud side to optimize the manufacturing service composition. By using Gaussian mixture distribution to approximate the service availability distribution, the proposed method can effectively model service uncertainty and obtain better composition solutions. Experimental results confirm the effectiveness of the algorithm.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Industrial
Bo Yang, Shilong Wang, Shi Li, Tianguo Jin
Summary: This paper proposes an optimal selection method to enhance the robustness of Cloud Manufacturing System during the planning stage. The gABC-GWO algorithm efficiently solves the robust service composition and optimal selection model, leading to improved robustness of CMS. Experimental results demonstrate significant enhancement in robustness with gABC-GWO algorithm compared to other common intelligence optimization algorithms.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Engineering, Industrial
Yuankai Zhang, Lin Zhang, Yongkui Liu, Xiao Luo
Summary: With the gradual adoption of cloud manufacturing platforms, issues such as trust and security have hindered enterprise migration. A proposed PoSP blockchain consensus protocol aims to address these challenges, reducing energy consumption and improving transaction speeds. The mechanism also introduces a credit incentive system, offering a new approach for trust evaluation within the cloud manufacturing system.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Review
Engineering, Industrial
Zhaojun Qin, Yuqian Lu
Summary: Mass personalization requires flexible manufacturing systems to meet changing demands, but current systems cannot adapt to dynamic production environments. To achieve flexible, autonomous, and error-tolerant production, a manufacturing system capable of self-optimization is needed. The concept of Self-Organizing Manufacturing Network is proposed as the next-generation manufacturing automation technology to achieve mass personalization.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Bo Yang, Shilong Wang, Qingqing Cheng, Tianguo Jin
Summary: Cloud manufacturing (CMfg) is a new manufacturing mode that dynamically allocates manufacturing resources based on demand. Research has mainly focused on factory manufacturing schemas, while field manufacturing schemas, which involve geographically dispersed resources for tasks like equipment assembly, are gaining more applications. The current trend towards complex products and service-oriented manufacturing industry has led to an urgent need for dynamic organization and global planning of dispersed manufacturing resources.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Information Systems
Yan Xiao, Congdong Li, Lijun Song, Jie Yang, Jiafu Su
Summary: This paper proposes a decision method for manufacturing service resource matching based on multidimensional information fusion, addressing the critical issues of how to quickly extract potential resources for distributed manufacturing service requirements, and how to carry out resource matching for manufacturing service requirements with correlated mapping characteristics. The method integrates information entropy, rough set theory, and a hybrid collaborative filtering algorithm to classify the importance of manufacturing service tasks and analyze matching capability, achieving precise quality, stable service, and maximum efficiency in resource matching decisions.