A Method for Optimizing Mountainous Highway Routes Based on Data Optimization Multi-Attribute Decision Model

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A Method for Optimizing Mountainous Highway Routes Based on Data Optimization Multi-Attribute Decision Model

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[1]GE Wei,PENG Zh aohui,XU Bo,et al.A Method for Optimizing Mountainous Highway Routes Based on Data Optimization Multi-Attribute Decision Model[J].Journal of Zhengzhou University (Engineering Science),2024,45(pre1):10-.[doi:10.13705/j.issn.1671-6833.2024.06.020]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 pre1 Page number: 10- Column: Public date: 2025-01-30

Title:: A Method for Optimizing Mountainous Highway Routes Based on Data Optimization Multi-Attribute Decision Model

Author(s):: GE Wei; PENG Zh aohui; XU Bo; Liu Mu; WANG Yawei; ZHANG Yadong; WANG Siwei; (1.School of Water C onservancy and T ransportationZhengzhou University, Zhengzhou 450001, China; 2.Sinohydro Bureau No.7 Co., Ltd, Zhengzhou 450001, China.)

Keywords:: mountainous highway; route optimization; cloud model; variable weight theory; topsis model

CLC:: U412.32

DOI:: 10.13705/j.issn.1671-6833.2024.06.020

Abstract:: There are problems in the optimization of mountainous highway routes, including complex evaluation indicators, difficulty in quantifying qualitative indicators, and the weight of subjectively set indicators do not match the actual situation. This article is based on the principles of technology, economy, and safety, referring to the Hall three-dimensional structure to analyze the influencing factors of mountainous highway routes, and constructs an evaluation index system. Introduced Cloud Model theory to quantify qualitative evaluation indicators, and used the Variable Weight theory to modify the constant weight of evaluation indicators considering the impact of the actual state of evaluation indicators on the evaluation results. Finally, a method for optimizing mountainous highway routes based on TOPSIS is proposed . This method was applied to the optimization of routes for the fourth risk point of the Alcia Highway Project in Bolivia. The results indicate that cloud models can effectively solve the problem of uncertainty in qualitative indicators, which makes them difficult to quantify; The closeness degree of the three route schemes is 0.833, 0.606, and 0.684, respectively. Compared with traditional constant weight, the variable weight theory highlights the influence of extreme indicators on the evaluation results in the evaluation process, and the results are more realistic

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