A Soft Rough-Fuzzy Preference Set-Based Evaluation Method for High-Speed Train Operation Diagrams

Abstract

This paper proposes a method of high-speed railway train operation diagram evaluation based on preferences of locomotive operation, track maintenance, S & C, vehicles and other railway departments, and customer preferences. The application of rough set-based attribute reduction obtains the important relative indicators by eliminating excessive and redundant evaluation indicators. Soft fuzzy set theory is introduced for the overall evaluation of train operation diagrams. Each expert utilizes a set of indicators during evaluation based on personal preference. In addition, soft fuzzy set theory is applied to integrate the information obtained via expert evaluation in order to obtain an overall evaluation. The proposed method was validated by a case study. Results demonstrate that the proposed method flexibly expresses the subjective judgments of experts while effectively and reasonably handling the uncertainty of information, which is consistent with the judgment process of humans. The proposed method is also applicable to the evaluation of train operation schemes which consist of multiple diagrams.

A Modified Combination Rule for D Numbers Theory

“…In the real world, it is difficult but necessary to make a comprehensive assessment to make a reasonable decision because much uncertainty and incompleteness are often involved in the assessments [1][2][3][4]. Several methods, such as probability theory [5,6], fuzzy theory [7][8][9][10][11][12][13], rough set [14][15][16][17], uncertainty theory [18][19][20], and Dempster-Shafer theory of evidence (DST) [21][22][23], are widely used to deal with these problems. These methods have widely been used in kinds of fields, like supplier selection [24,25], risk assessments [26][27][28][29], and so on [30,31].…”

A Cost and Passenger Responsible Optimization Method for the Operation Plan of Additional High-Speed Trains in a Peak Period

“…Passenger responsible models for LPP focus on maximizing the level of service for passengers, that is, the number of direct travelers, travel times, and waiting times of all passengers [13][14][15]. Cost and passenger responsible models give a trade-off between the cost of the railway company and the satisfaction of passengers [8,[16][17][18]. Passenger demand is always ignored in literatures of adding trains.…”

Evaluation of the maintenance window scheme using fuzzy soft set theory

“…Combining Eq. 8to (10) and calculating the weight values of the synthesized parameter lead to the results given in Table 8. The weight value indicates the degree of importance of each parameter after synthesis.…”

“…Second, fuzzy comprehensive evaluation, AHP, and other commonly researched methods require each evaluation expert to consider the same set of evaluation indicators and give evaluation information for each MWS. However, evaluation experts often come from different areas, or from different organizations and departments, and each expert has different knowledge and experience [10]. Therefore, each evaluation expert may only focus on a few interesting or familiar indicators.…”