TOPSIS model with combination weight for demand assessment of flood emergency material supplies

Bingbing Xu; Wenguang Yang; Lanxiang Yi; Dekun Kong; Ruitian Liu; Bingbing Xu; Wenguang Yang; Lanxiang Yi; Dekun Kong; Ruitian Liu

doi:10.3934/math.2025248

AIMS Mathematics

2025, Volume 10, Issue 3: 5373-5398. doi: 10.3934/math.2025248

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TOPSIS model with combination weight for demand assessment of flood emergency material supplies

College of Science, North China Institute of Science and Technology, Beijing 101601, China

Received: 06 January 2025 Revised: 24 February 2025 Accepted: 27 February 2025 Published: 10 March 2025
MSC : 62P12, 62P25

Assessing the urgency of emergency material demand in disaster scenarios improves dispatching efficacy and enhances emergency management agencies' operational efficiency during post-disaster relief. Taking the July 20 flood in Henan, China, as a case study, this paper assesses the urgency of emergency material demand in flood disaster scenarios. The objective weights and subjective weights of the evaluation indicators are calculated using the coefficient of variation method and the order relation analysis method, respectively. Then this paper combines these calculated weights on the basis of maximizing deviation, and the technique for order preference by similarity to an ideal solution (TOPSIS) method is used to judge the demand urgency of disaster sites. Finally, a cloud model is introduced to visualize the urgency evaluation results obtained from the single weighting method and the combination weighting method by generating cloud maps. The results demonstrate that the hyper-entropy value of the cloud digital features obtained by the combination weighting is 0.0432 (the smallest among the methods), indicating the least uncertainty and a relatively small degree of dispersion. At the same time, the condensation rate of cloud droplets in the cloud map generated by the combined weighting method is higher, indicating that the combined weighting method has lower uncertainty compared with the single weighting method and is superior to the single weighting method in efficiency. Moreover, through sensitivity analysis, it is evident that when the weight of the most important evaluation indicator varies within the range of [0.1192, 0.3881], the TOPSIS method based on combined weighting demonstrates strong robustness.
- flood disaster,
- demand urgency,
- combination weighting method,
- single weighting method,
- TOPSIS,
- cloud model
Citation: Bingbing Xu, Wenguang Yang, Lanxiang Yi, Dekun Kong, Ruitian Liu. TOPSIS model with combination weight for demand assessment of flood emergency material supplies[J]. AIMS Mathematics, 2025, 10(3): 5373-5398. doi: 10.3934/math.2025248

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Abstract

Assessing the urgency of emergency material demand in disaster scenarios improves dispatching efficacy and enhances emergency management agencies' operational efficiency during post-disaster relief. Taking the July 20 flood in Henan, China, as a case study, this paper assesses the urgency of emergency material demand in flood disaster scenarios. The objective weights and subjective weights of the evaluation indicators are calculated using the coefficient of variation method and the order relation analysis method, respectively. Then this paper combines these calculated weights on the basis of maximizing deviation, and the technique for order preference by similarity to an ideal solution (TOPSIS) method is used to judge the demand urgency of disaster sites. Finally, a cloud model is introduced to visualize the urgency evaluation results obtained from the single weighting method and the combination weighting method by generating cloud maps. The results demonstrate that the hyper-entropy value of the cloud digital features obtained by the combination weighting is 0.0432 (the smallest among the methods), indicating the least uncertainty and a relatively small degree of dispersion. At the same time, the condensation rate of cloud droplets in the cloud map generated by the combined weighting method is higher, indicating that the combined weighting method has lower uncertainty compared with the single weighting method and is superior to the single weighting method in efficiency. Moreover, through sensitivity analysis, it is evident that when the weight of the most important evaluation indicator varies within the range of [0.1192, 0.3881], the TOPSIS method based on combined weighting demonstrates strong robustness.

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