Open AccessRailway route selection based on entropy weight method-gray correlation improvement TOPSIS
Author(s) -
Ying Zhou,
Xixiao Liu,
Feng Li,
Wen Jiang
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/304/3/032112
Subject(s) - topsis , gray (unit) , entropy (arrow of time) , computer science , correlation , mathematical optimization , correlation coefficient , cross entropy , data mining , mathematics , operations research , principle of maximum entropy , artificial intelligence , machine learning , medicine , physics , geometry , quantum mechanics , radiology
In order to reasonably select the best railway route design scheme, the paper research the comprehensive evaluation model of TOPSIS based on entropy weight method-gray correlation. The paper establish the comprehensive evaluation index system from four aspects: technology, economy, society and environment. Then set up railway route selection model of gray correlation improvement TOPSISI, and use entropy weight method to determine the weight coefficient of each index. Finally, take the rail connection project as the example for verification. Making comparison of three alternative route design by using this model, respectively obtain the three scheme relative proximity are 0.575/0.543/0.551. The relative proximity of scheme 1 is the largest (0.575), which is the optimal design scheme, and it is consisted with the actual connection scheme. The paper use this model for railway route selection can make full use of objective data information and reduce the influence of subjective factors.