Görçün, Ömer FarukGorcun, Omer FarukPamucar, DraganKucukonder, Hande2024-06-232024-06-23202451568-49461872-9681https://doi.org/10.1016/j.asoc.2023.111127https://hdl.handle.net/20.500.12469/5664küçükönder, hande/0000-0002-0853-8185The wrong design of rail system vehicle fleets is one of the most critical problems in terms of urban transportation. Light rail system fleets in many large cities, including Istanbul, consist of various types and feature vehicles. It creates significant problems in integrating each rail system vehicle into the system. In addition, while it is necessary to keep an inventory of spare parts for each different type of vehicle, it requires different qualifications for professionals involved in processes such as maintenance and repair, leading to extra costs. In this context, the study's primary purpose is to determine the most suitable light rail vehicles for urban transportation systems and create vehicle fleets accordingly. In that regard, the study aims to provide a reliable and practical decision-making model that can be used as a roadmap for decision-makers when choosing tramcars to solve these problems. The present work proposes a hybrid procedure integrating the Best and Worst Method (BWM) and Power-Heronian Weighted Aggregated Sum Product Assessment (WASPAS'PH) approaches. The most critical implication of the work indicated that the acquisition cost per tramcar set (0.148) is still the most influential factor. The economic lifespan of tramcars (0.037), the number of seats in a vehicle set (0.041), and energy consumption (0.072) have followed the most significant criterion, respectively. Besides, it highlighted that the A14 Brand CR (0.7819) is the most appropriate option for well-structuring the urban light rail system fleet. An extensive validation test to check the suggested model's robustness confirmed the procedure's stability and consistency.eninfo:eu-repo/semantics/closedAccessTramcar choiceBWMWASPAS 'PHPower averageHeronian functionsPublic transitArticle151WOS:00114346870000110.1016/j.asoc.2023.1111272-s2.0-85181034105Q1Q1