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Original scientific paper

https://doi.org/10.7906/indecs.22.5.7

Determining the Most Suitable Bus Model for the Urban Transportation Sector: A Hybrid Multi-Criteria Decision-Making Approach

Hakan Murat Arslan ; Duzce University, Faculty of Business, Department of Management and Information Systems, Düzce, Türkiye *
İsmail Durak ; Duzce University, Faculty of Business, Department of Management and Information Systems, Düzce, Türkiye
Ece Yavuzyilmaz ; Duzce University, Faculty of Business, Department of Management and Information Systems, Düzce, Türkiye

* Corresponding author.


Full text: english pdf 1.244 Kb

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Abstract

One of the key service elements of travel companies operating in the road transportation sector are passenger buses. Deciding to purchase a new bus requires managers to consider various aspects, complicating the decision-making process. In this context, implementing Multi-Criteria Decision Making (MCDM) methodologies in such decision problems can provide more accurate and effective solutions. The aim of this study is to investigate the new bus purchase decision processes of travel companies operating in Turkey with MCDM methods and to determine the most suitable bus model. Ten major bus models were analyzed in the study using four different MCDM approaches based on four major criteria and 16 sub-criteria. The weights of the criteria used in the evaluation of the relevant decision problem were determined by the Entropy method. Moreover, WASPAS, EDAS, and Gray Relational Analysis were utilized separately to discover the best suitable alternative. Furthermore, the rankings generated by these several MCDM approaches were compared using Spearman Rank Correlation Analysis in the study, and they were found to be highly consistent. The study provides several key recommendations for academic research and industry practice moving forward.

Keywords

intercity passenger transport; entropy method; EDAS method; WASPAS method; grey relational analysis method

Hrčak ID:

321872

URI

https://hrcak.srce.hr/321872

Publication date:

30.10.2024.

Visits: 20 *