Research on the operation safety evaluation of urban rail stations based on the improved TOPSIS method and entropy weight method

Wu, Hua-Wen; Li, En-qun; Sun, Yuanyun; Dong, Baotian

doi:10.1016/j.jrtpm.2021.100262

Cited by 24 publications

(6 citation statements)

References 15 publications

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“…However, if the subjective preference can be ignored, then the entropy method can be used to represent the attribute weights (Lotfi & Fallahnejad, 2010;Chen, 2021). Due to its simplicity, its application within MADM is widespread (e.g., Kaviani et al, 2020;Wu et al, 2021;Yusuf et al, 2022).…”

Section: The Entropy Methodsmentioning

confidence: 99%

Supplier Selection in Family Small and Medium Enterprises

Mantos

Layon

Beduya

et al. 2023

International Journal of Service Science, Management, Engineering, and Technology

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Current literature on family businesses and small and medium enterprises (SMEs) offers some disintegrated insights in view of managing supply chains, particularly the supplier selection agenda. The presence of distinct characteristics inherent to both enterprises calls for a new line of inquiry regarding supplier selection for family SMEs. This work advances the literature by (1) identifying a set of supplier selection attributes best encapsulating the interests of family SMEs, and (2) evaluating these attributes to guide relevant decision-making. With a case study in the food industry and previous lists of supplier selection attributes, ten attributes were considered relevant to family SMEs. Applying the hybrid entropy-MARCOS method yields the priority attributes in decreasing order: on-time delivery, total service quality, product quality, productivity, attitude, response to customer requests, problem-solving capacity, payment terms, price, and flexibility. An analysis with other comparable methods suggests high consistency of these findings. Theoretical insights were discussed.

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Section: The Entropy Methodsmentioning

confidence: 99%

Supplier Selection in Family Small and Medium Enterprises

Mantos

Layon

Beduya

et al. 2023

International Journal of Service Science, Management, Engineering, and Technology

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“…The greater the dispersion degree, the greater the influence of this index on the comprehensive evaluation. The entropy value can be used to judge the dispersion degree of a certain index, which provides a basis for a multi-index system [39,40]. The entropy method is generally divided into four steps.…”

Section: Entropy Methods and Environmental Regulationmentioning

confidence: 99%

Impact of Environmental Regulation and Industrial Agglomeration on Carbon Emissions in the Yangtze River Economic Belt

et al. 2023

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Carbon reduction is an important aspect of achieving high-quality environmental development; environmental regulation and industrial agglomeration are important ways to affect carbon emissions. Therefore, studying the relationship between industrial agglomeration, environmental regulation, and carbon emissions has important theoretical and practical significance. Firstly, this article adopts the method of location entropy to measure the indicators of industrial agglomeration. Secondly, it proposes an environmental regulation indicator system based on the experience of previous scholars and measures the environmental regulation indicators using the entropy method. Next, eight types of energy consumption are used to measure carbon emissions based on the carbon emission coefficient method provided by the IPCC. Finally, based on the Moran index, the spatial correlation of carbon emission levels in various regions of the Yangtze River Economic Belt (YREB) is tested. A spatial econometric model was introduced to explore the relationship between industrial agglomeration, environmental regulation, and carbon emissions at a deeper level, and the following conclusions were drawn: (1) The regression coefficient of the spatial term of industrial agglomeration on carbon emissions is 0.848, which is significantly positive at the 10% level, indicating that under the influence of spatial effects, industrial agglomeration has a significant promoting effect on carbon emissions. (2) The regression coefficient of the spatial term of environmental regulation on carbon emissions is −0.011, which is significantly negative at the 10% level, indicating that environmental regulation has an inhibitory effect on carbon emissions under the influence of spatial effects. Based on the above conclusions, useful suggestions have been provided for optimizing industrial structure, improving environmental regulation levels, and alleviating carbon emission issues.

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“…Among them, the entropy method is an objective weighting evaluation method, which can effectively overcome the subjectivity of artificially determining the weights and the overlap of information among multiple indicators [ 41 , 42 ]. This method is suitable for multi-index quantitative evaluation, and has been applied in different fields such as urbanization comprehensive evaluation, tourism competitiveness evaluation, and cultivated land ecological security evaluation [ 43 ]. Meanwhile, in order to overcome the disadvantages of extreme value or negative value in the measurement results of traditional entropy method, the index data are processed by using standardized transformation method (i.e., the improved entropy method).…”

Section: Methodsmentioning

confidence: 99%

Spatial Differentiation and Tradeoff–Synergy of Rural Multifunction at the County Scale in Anhui Province in the China’s Traditional Agricultural Areas

Zhang

2022

IJERPH

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The study of rural multifunction interaction has ignored the spatial differentiation characteristics, so it is necessary to reveal in-depth the spatial interaction relationship of rural multifunction interaction on the basis of type division and pattern recognition at the county scale. Taking Anhui as a case study, based on the county scale, the paper constructed an index system of rural multifunction evaluation, and comprehensively applied the improved entropy method, spatial autocorrelation model, and Spearman correlation coefficient to study the temporal-spatial characteristics and tradeoff–synergy relationship of rural multifunction from 2000 to 2020. The results showed that (1) the overall rural production function at the county level in Anhui showed an upward trend, showing a spatial pattern of “low in the south and high in the north”. The rural life function was gradually weakened, showing the characteristics of gradual decline from suburban to exurb. The rural ecological function increased, showing a high value distribution in areas with rich mountains and hills or a dense water network. (2) The spatial concentration of HH (High-High) and LL (Low-Low) positive correlation types of rural production, life, and ecological function was significant during the study period. The negative correlation types of HL (High-Low) and LH (Low-High) had low spatial agglomeration and showed discrete distribution. (3) There was a spatial synergistic relationship between rural production and life function at the county scale in Anhui Province. The tradeoff–synergy relationship between rural production and ecological function and between rural life and ecological function showed a trend of synergistic–tradeoff fluctuation. The spatial difference of the rural multifunction tradeoff–synergy relationship was significant during the study period. (4) In the future, we should change the situation that the expansion of rural production function compresses rural life and ecological function, and promote rural life and ecological function to promote the benign and coordinated development of rural multifunction at the county scale in traditional agricultural areas.

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Research on the operation safety evaluation of urban rail stations based on the improved TOPSIS method and entropy weight method

Cited by 24 publications

References 15 publications

Supplier Selection in Family Small and Medium Enterprises

Supplier Selection in Family Small and Medium Enterprises

Impact of Environmental Regulation and Industrial Agglomeration on Carbon Emissions in the Yangtze River Economic Belt

Spatial Differentiation and Tradeoff–Synergy of Rural Multifunction at the County Scale in Anhui Province in the China’s Traditional Agricultural Areas

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