As a result of the continuous improvement in passengers’ requirements for the quality of train operation, the comfort of high-speed train operation has been paid increasing attention. The evaluation of comfort has gradually changed from the narrow sense of a comfort evaluation model containing only vibration to the generalized evaluation of passengers’ overall satisfaction with the ride environment of specific lines. The factors affecting comfort evaluation include physical, physiological, and psychological aspects. To address the problems that the existing comfort evaluation model has a single index and that the weight determination of some indicators is greatly affected by subjectivity, we built a high-speed train comfort evaluation model based on variable weight theory. Combined with the actual working conditions of the Baolan passenger dedicated line, dynamic detection data and noise monitoring data captured by a track inspection car were combined with a passenger ride comfort questionnaire survey. In addition, the initial weight value of each factor was optimized by constructing an equilibrium function to realize the balance between the various factors, so as to realize the comprehensive fuzzy evaluation of high-speed train comfort. The results show that the comprehensive evaluation result of the comfort degree of the high-speed train on the Tongwei to Lanzhou section of the Baolan passenger dedicated line has a grade of II. The fuzzy scores of the evaluations using variable weights and constant weights were analyzed from the perspective of membership degree. The variable weight optimization avoids the one-sidedness and extremeness of the constant weight calculation. The comprehensive evaluation results are closer to the real situation. The research results can provide a reference for the comfort evaluation of high-speed trains with extreme differences in state values and constant weights and help in the acquisition of more realistic evaluation results.

The optimization of railway construction schemes is a complexity system engineering task with multiple dimensions, diverse conditional constraints, and multifaceted objective assessments. The decision-making and scheme evaluation entail subjectivity, randomness, and fuzziness. To address the comprehensive optimization challenge in construction schemes effectively and efficiently, we investigate an optimization method for railway construction schemes. This method is based on multi-dimensional combination weighting and improved grey theory. After analyzing the primary influencing factors, we established a railway construction plan optimization index system comprising 4 dimensions and 18 factors. The weight combination coefficient is determined using the pros and cons solution distance method, and the optimal weight set for the index is determined through the multi-dimensional combination weighting approach. Utilizing the method of superior and inferior solution distance coupled with grey theory, we ascertain the order of advantages and disadvantages for each construction scheme, subsequently achieving construction scheme optimization. To illustrate this, we employ the optimization process for a high-speed railway section in Guangxi as an exemplar. The verification results indicate that the gray relative closeness values for schemes A, B, and C are 0.7089, 0.4813, and 0.4463, respectively. Scheme A has the highest gray relative closeness value, thus making it the optimal route scheme. The optimal results obtained through this method align with the outcomes of expert validation and existing research, thereby validating the effectiveness and practicality of the model. By employing a multidimensional combination weighting method, the deficiencies of traditional indicator weight calculations are mitigated, resulting in indicator weights that are more reflective of the actual circumstances. At the same time, the application of improvements in the grey theory comprehensive evaluation method enables the integration and computation of indicator data for each construction plan. Through the intuitive representation of grey relative closeness, the advantages and disadvantages of each plan are effectively characterized. This enhances the scientific rigor and applicability of the railway construction plan optimization process. The research findings can serve as a reference for similar railway construction scheme selection problems in the future.