Analysis of service efficiency indices for the Romanian Division A1 women's volleyball teams in the 2019-2020 season

2020 ◽  
Author(s):  
Nicolae Lucian Voinea ◽  
Gloria Rața
2021 ◽  
pp. 1-13
Author(s):  
Sun Jianzhu ◽  
Zhang Qingshan ◽  
Yu Yinyun

Multi-site selection is a hot research issue for equipment manufacturing enterprises. With the development of smart industry, equipment manufacturing enterprises have entered the era of personalized and small batch manufacturing. Enterprises want to better meet customer needs and win competition, they must carry out scientific factory planning and site selection, so as to ensure quick response to the market. Based on this, this paper proposes a two-stage location selection model. Firstly, the method uses fuzzy numbers to express the demand size of demand points. Secondly, the distance factor is used as a criterion to select the candidate manufacturing bases with sufficient available resources. Next, the location model of enterprise manufacturing base is established which the goal of maximizing service efficiency and the constraints of time, cost and demand. Finally, a random numerical example is used to simulate the model, and lingo is used to solve it.


1969 ◽  
Vol 14 (3) ◽  
pp. 484
Author(s):  
Robert B. Fetter ◽  
Martin S. Feldstein

2021 ◽  
Vol 17 (3) ◽  
pp. 50-62
Author(s):  
Ayodeji Samuel Makinde ◽  
Abayomi O. Agbeyangi ◽  
Wilson Nwankwo

Mobile number portability (MNP) across telecommunication networks entails the movement of a customer from one mobile service provider to another. This, often, is as a result of seeking better service delivery or personal choice. Churning prediction techniques seek to predict customers tending to churn and allow for improved customer sustenance campaigns and the cost therein through an improved service efficiency to customer. In this paper, MNP predicting model using integrated kernel logistic regression (integrated-KLR) is proposed. The Integrated-KLR is a combination of kernel logistic regression and expectation-maximization clustering which helps in proactively detecting potential customers before defection. The proposed approach was evaluated with five others, mostly used algorithms: SOM, MLP, Naïve Bayes, RF, J48. The proposed iKLR outperforms the other algorithms with ROC and PRC of 0.856 and 0.650, respectively.


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