Optimal Selection of Mountain Railway Location Design Based on Twice-Improved TOPSIS Method

This work aims to overcome the shortcomings of the traditional TOPSIS (technique for order of preference by similarity to an ideal solution) method, which cannot exclude the interference from correlation among indexes that can easily complicate decision-making operations during optimal selection of mountain railway location designs, and thus enable full consideration of any uncertain characteristics during decision-making. First, the Mahalanobis distance was used to replace the Euclidean distance in traditional TOPSIS, thus achieving the first improvement in TOPSIS. Then, the correlation coefficient matrix was used to replace the covariance matrix in the Mahalanobis distance, which represents the second improvement in the TOPSIS method. Then, linguistic fuzzy numbers, interval numbers, and a cloud model were used to achieve quantification of the qualitative indexes, and a comprehensive optimization model for mountain railway location design was constructed based on the twice-improved TOPSIS method. Finally, the comprehensive optimization model was applied to the partial route direction scheme of the Batang to Changdu section of a specific mountain railway. The results show that the twice-improved TOPSIS method can effectively exclude the interference from the correlation among indexes and thus simplifies the decision-making process. In addition, application of the cloud model can overcome the deficiency in the decision-making method with regard to its ability to deal with the uncertainty of qualitative language. The application results from the comprehensive optimization model coincide with the recommended results of the pre-feasibility study of the mountain railway, i.e., the optimization results from both approaches lead to the route scheme passing through Baiyu and Jiangda, thus indicating that the proposed model can be used as a new approach for route direction optimization in future mountain railway location designs.