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Congdong Li

Bio: Congdong Li is an academic researcher from Macau University of Science and Technology. The author has contributed to research in topics: Matching (statistics) & Cloud manufacturing. The author has an hindex of 1, co-authored 1 publications receiving 24 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, a matching decision method for manufacturing service resources is proposed based on multidimensional information fusion, where the information entropy and rough set theory are applied to classify the importance of manufacturing service tasks, while the matching capability are analyzed by using a hybrid collaborative filtering algorithm.
Abstract: With the development of specialization, coordination and intelligence in the manufacturing service process, the issue of how to quickly extract potential resources or capabilities for distributed manufacturing service requirements, and how to carry out resource matching for manufacturing service requirements with correlated mapping characteristics, have become the critical issues to be addressed in the cloud manufacturing environment. Through the combination of the characteristics of relevance, synergy and diversity of manufacturing service tasks on the intelligent cloud platform, a matching decision method for manufacturing service resources is proposed in this paper based on multidimensional information fusion. On the basis of integrating multidimensional information data in cloud manufacturing resource, the information entropy and rough set theory are applied to classify the importance of manufacturing service tasks, while the matching capability are analyzed by using a hybrid collaborative filtering (HCF) algorithm. Then, the information of function attribute, reliability and preference is employed to match and push manufacturing service resources or capabilities actively, so as to realize the matching decision of manufacturing service resources with precise quality, stable service and maximum efficiency. At last, a case study of resources matching decision for body & chassis manufacturing service in a new energy automobile enterprise is presented, in which the experimental results show that the proposed approach is more accuracy and effective compared with other different recommendation algorithms.

47 citations


Cited by
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Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper empirically examined the impact of the degree of resource slack on enterprise environmental protection investment and the moderating effect of environmental management maturity on both of them.

23 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors analyzed the trends and causes of customer churn through data mining algorithms, and gave the answers to such questions as how the customer churn occurs, the influencing factors of user churn, and how enterprises win back churned customers.
Abstract: Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain “high-value” customers, and continue to provide customers with “value” and reduce the cost of maintaining customers.

13 citations

Journal ArticleDOI
TL;DR: In this article , the optimal production and subsidy rate of a three-player supply chain considering consumer environmental awareness (CEA) is investigated, and the authors developed a differential game based model to explore the optimal subsidy rate considering government's different goals of social welfare maximization and governmental utility maximization.

13 citations

Journal ArticleDOI
TL;DR: In this paper, the authors explored the relationships among employees' politeness strategies, customer membership, perceived co-recovery, and online post-covery satisfaction, and provided valuable suggestions for online service providers to improve online recovery performance.

11 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a data-driven peer-to-peer blockchain framework to predict future water consumption, which utilizes a blockchain system with a peer topeer network to serve as the decision support platform hardware.
Abstract: It is widely believed that effective water resource management can optimize the scheduling of water supply plans, which is essential for sustainable development. The core of management is to accurately predict future water consumption. However, existing studies generally face two challenges. First, a reliable bottom platform for the support of online data integration is absent. In addition, multisource factors that primarily affect water consumption are neglected when modeling. To solve the above problems, this paper proposes a data-driven peer-to-peer blockchain framework to predict water consumption. Fundamentally, it utilizes a blockchain system with a peer-to-peer network to serve as the decision support platform hardware. On this basis, an intelligent prediction algorithm that combines the grey model and long short-term memory model is developed to drive the hardware infrastructure. After that, the performance of the proposed method is evaluated by carrying out experiments on a real-world dataset, and three typical approaches are selected for comparison. The experimental results show that the proposal exceeds general prediction models by approximately 8%.

11 citations