Logistics service supply chain order allocation mixed K-Means and Qos matching

Zhang, Shuiwang; Bi, Cancan; Zhang, Min

doi:10.1016/j.procs.2021.05.060

Cited by 7 publications

(11 citation statements)

References 16 publications

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“…These logistics needs can be divided into three categories: basic logistics needs, value-added logistics needs, and high-end logistics needs (Durach et al, 2017). The logistics industry demands to improve service level, shorten supply chain response time, reduce logistics cost, and realize the supply chain solution design and planning (Zhang et al, 2021). In the face of increasingly fierce market competition, the demand of the logistics industry focuses on strengthening services, shortening the response time of the supply chain, reducing the logistics cost of the supply chain, and achieving supply chain improvement.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Supply chain operation evaluation and management decision by fuzzy cognitive map model

Zheng

2022

Expert Systems

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The components of the logistics and supply chain are analysed to meet the needs of supply chain management and decision support, aiming at the complexity and uncertainty of the supply chain system. First, the characteristics of logistics demand and logistics service types of some enterprises are analysed. The factors affecting supplier selection are obtained through the questionnaire survey of some enterprises. Second, the mutual feedback relationship between the influencing factors of logistics service demand is analysed, and a fuzzy cognitive graph model of the selection process of enterprise logistics service providers is established. Finally, the fuzzy cognitive map (FCM) model is used to conduct an empirical study on the choice of logistics service providers. The results show that the ranking results of the three candidate logistics service companies based on FCM are WL1 (0.7233) > WL3 (0.6823) > WL2 (0.6764). When each node of the concept does not affect each other, the ranking result of the three candidate logistics service companies based on analytic hierarchy process (AHP) is WL1 (0.7265) > WL2 (0.6949) > WL3 (0.6920). The final evaluation result will be affected by the mutual influence relationship between the concept nodes. Overall, the lowest evaluation score is WL2, and the highest is WL1. The most suitable logistics service provider for the enterprise is WL1. The FCM model selected by the manufacturing enterprise logistics service provider is reasonable and feasible. The mutual feedback between evaluation factors affects the selection of logistics service providers in a company. The FCM can get a more accurate evaluation weight value and can realize the optimal choice of logistics suppliers. It provides reference and decision support for other enterprises.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Supply chain operation evaluation and management decision by fuzzy cognitive map model

Zheng

2022

Expert Systems

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“…Step 2: Calculate the value of E Step 3: Update the centres of each cluster and calculate the new E Step 4: Check whether the convergence condition is satisfied Step 5: Output the parameters and finish if yes, and skip to Step 2 if no All division-based clustering algorithm methods have a significant problem [26][27][28]: they are sensitive to the initial clustering centre. erefore, for K-means clustering algorithms, the initial clustering centre is a key factor in determining the quality of clustering.…”

Section: Basic Principles Of K-meansmentioning

confidence: 99%

Machine Learning-Based Emotion Factor Analysis of Sport Fan Community

Heng-chuan

Liu

Zhen

2022

Security and Communication Networks

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Football is one of the most popular sports in the world. As the popularity of football continues to grow worldwide, so does the number of incidents of violence on the pitch. Today, doping, match fixing, black whistles, and football hooliganism are ranked as the four most toxic aspects of sport. How to study the factors that cause aggressive behaviour of fans from a psychological perspective has become a key issue in the field of sports. Therefore, this study proposes a method for mining the psychological factors of sport fan community members based on machine learning clustering. Firstly, three different members of a large fan community, i.e., university students, office workers, and unemployed people, are used as research subjects to investigate the psychological factors influencing fans’ aggressive behaviour using a questionnaire method. Secondly, the data obtained were mined and analysed using the K-means clustering algorithm in machine learning techniques. At the same time, a K-means initial clustering centre optimization algorithm based on principal component analysis (PCA) was proposed for the data characteristics of the interaction of psychological factors. The results show that the new algorithm significantly improves the quality of clustering compared with other optimization algorithms and accurately identifies the multiple factors that contribute to the occurrence of fan attacks.

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“…LSSC is a self-centered inter-enterprise network connected by the competition and cooperation business relationship between its members in the market. It is a new type of logistics service based on alliance developed in practice [2,10]. It guarantees the logistics operation of the product supply chain by providing flexible logistics services [2,11].…”

Section: Lsscmentioning

confidence: 99%

“…It is too hard to meet the market demand for a single logistics enterprise, which develops logistics service supply chain (LSSC for short). LSSC has received more and more attention [1][2][3][4][5]. LSSC is a kind of new mode based on the alliance of logistics services, with "functional logistics service subcontractor→functional logistics service provider→logistics services integrator→customer" as the basic structure.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Effects of Network Structure Attributes on Growth Performance of Logistics Service Integrators in Logistics Service Supply Chain: Empirical Evidence

2022

Sustainability

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The purpose of this paper is to investigate the relationship between network structure attributes, sensing capability and growth performance of logistics service integrators for logistics service supply chain. Using survey research of logistics service integrators from China, through empirical analysis, this paper finds the following conclusions. Network structure attributes can affect logistics service integrators growth performance directly or indirectly through sensing capability: ① network size and centricity affect the growth performance by promoting sensing capability; ② heterogeneity can directly affect LSI growth performance; and ③ heterogeneity can affect growth performance indirectly. The network scope does not affect sensing capability and growth performance of logistics service integrators.

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Logistics service supply chain order allocation mixed K-Means and Qos matching

Cited by 7 publications

References 16 publications

RETRACTED: Supply chain operation evaluation and management decision by fuzzy cognitive map model

RETRACTED: Supply chain operation evaluation and management decision by fuzzy cognitive map model

Machine Learning-Based Emotion Factor Analysis of Sport Fan Community

The Effects of Network Structure Attributes on Growth Performance of Logistics Service Integrators in Logistics Service Supply Chain: Empirical Evidence

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