Evaluation of Wheelset Re-profiling Strategies Based on Combination Weighting TOPSIS

Abstract

In view of the subjective and single problem of the current evaluation methods of train re-profiling strategies, taking the single-wheel re-profiling strategies of CRH380D trains as an example, a new evaluation is proposed and it combines entropy weight, the weight of analytic hierarchy process, and the Preference for Order Preference by Similarity to an Ideal Solution (TOPSIS). Firstly, establish a multi-objective decision-making model and evaluation indicators based on the principle of wheelset re-profiling strategy. Secondly, use Monte Carlo simulation to simulate the single-wheel repair strategies and the corresponding expected value. Then, the objective weight of entropy is established based on the re-profiling strategies, the subjective weight of the analytic hierarchy process is established based on the importance of the three indicators, and the subjective and objective weights are combined to form a new evaluation method with TOPSIS. Finally, the traditional evaluation method and the combined weight TOPSIS evaluation method are used to evaluate the wheelset re-profiling strategy. The results show that compared with the traditional evaluation method, the average value of the combined weighted TOPSIS evaluation value is increased by 31.6%, the standard deviation is reduced by 30.6%, and its stability and reliability are significantly improved.