Intelligent Manufacturing in the Context of Industry 4.0: A Review

Integer programming formulations of discrete hub location problems

Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities

Spatiotemporal differentiation and the factors influencing urbanization and ecological environment synergistic effects within the Beijing-Tianjin-Hebei urban agglomeration

In this paper, the frequently used normalization methods for the entropy method (EM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) when these two methods are used in combination with each other are summarized. Taking information entropy (IE) as an indicator to measure the diversity of attribute data (DAD), the effects of normalization on the entropy-based TOPSIS method are analyzed. It is found that normalization can affect the decision result by affecting the DAD, while the DAD affects the contribution of attributes to the distance of each alternative from the ideal solution and the negative ideal solution, manifested in that the higher the DAD is, the bigger the contribution of the attribute will be. It is proved that vector normalization (VN) and sum normalization (SN) will not change the DAD, whereas min-max normalization (MMN) not only will change the DAD, but also may cause the appearance of numerous zero values, finally resulting in the fact that the calculated results of IE cannot represent the diversity of raw data. Therefore, normalization is not suggested for the EM, and VN is suggested for TOPSIS method. Some discussions on the combinability between the EM and TOPSIS method are also given as well as the applicability of different normalization methods in TOPSIS method.

Effects of normalization on the entropy-based TOPSIS method

Urban rail transit system operation performance evaluation results are important for government, transit operators and passengers. In this paper, we formulate an entropy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Method to evaluate the urban rail transit system’s operation performance from the operator’s, passenger’s and government’s perspectives. Firstly, we establish the evaluation indicator system with 8 indicators and a total of 41 sub-indicators, the operational data of the 41 sub-indicators will be used as the input of the approach. Second, we formulate our new approach to obtain the performance evaluation: the Entropy Weight Method (EWM) will be used to calculate the weight of each sub-indicator; the product of the corresponding probability and weight will be used to formulate the performance from operator’s, passenger’s and government’s perspectives; the TOPSIS will be used to calculate the comprehensive evaluation values and rankings of performance for each month. Third, the Chengdu subway with 34 months initial data will be chosen as the case study to test our new approach, the related suggestions to the government, to the passengers as well as the operators and managers will also be given.

Using entropy-TOPSIS method to evaluate urban rail transit system operation performance: The China case

High-speed railway (HSR) express freight train service sites selection is critical to the development of China’s Third Party Logistics market. In this paper, we formulate an improved entropy-cloud model based approach to solve the HSR express sites selection problem for the first time. The basic data of the indicators, for example, existing railway network conditions, traffic environment, express freight market demand, and policy, will be used as the inputs. We apply improved entropy method to obtain each subindicator’s weight. The cloud model will be used to select and classify each station under evaluation.

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Using Improved Entropy-Cloud Model to Select High-Speed Railway Express Freight Train Service Sites

Reasonable railway container freight stations layout means higher transportation efficiency and less transportation cost. To obtain more objective and accurate reselection results, a new entropy-cloud approach is formulated to solve the problem. The approach comprises three phases: Entropy Method is used to obtain the weight of each subcriterion during Phase  1, then cloud model is designed to form the evaluation cloud for each subcriterion during Phase  2, and finally during Phase  3 we use the weight during Phase  1 to multiply the initial evaluation cloud during Phase  2. MATLAB is applied to determine the evaluation figures and help us to make the final alternative decision. To test our approach, the railway container stations in Wuhan Railway Bureau were selected for our case study. The final evaluation result indicates only Xiangyang Station should be renovated and developed as a Special Transaction Station, five other stations should be kept and developed as Ordinary Stations, and the remaining 16 stations should be closed. Furthermore, the results show that, before the site reselection process, the average distance between two railway container stations was only 74.7 km but has improved to 182.6 km after using the approach formulated in this paper.

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Bin Shuai

Papers

Using entropy-TOPSIS method to evaluate urban rail transit system operation performance: The China case

Using Improved Entropy-Cloud Model to Select High-Speed Railway Express Freight Train Service Sites

Railway Container Station Reselection Approach and Application: Based on Entropy-Cloud Model