Ranking Iran's International Rail Transportation Companies Using Dynamic Multi-criteria Decision-making and Fuzzy Inference System

Railway cargo transportation has gained popularity as a preferred mode of conveyance, owing to its benefits over alternative methods. These advantages include reduced pollution in comparison to road transport, shorter transit times compared to maritime shipping, and cost-effectiveness in contrast to air transport. As a result, transport companies are more willing to use rail freight transport, especially for international transportation. One of the challenges facing the Islamic Republic of Iran Railways is to rank these companies, which are influential in various decisions such as granting discounts and facilities, handing over foreign wagons, prioritizing requests, and handling complaints. Therefore, this paper aims to develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies.

Considering the dynamic nature of the problem, the dynamic multi-criteria decision-making model was implemented based on the real data of international freight transportation (including export, import, and transit) by international rail transport companies in five years. To estimate future data for international rail transport companies, the fuzzy inference system was used. A fuzzy inference system is a mathematical tool that can handle uncertainty and imprecision in data.

Considering the dual uncertainty factors initially, companies' activities were evaluated using a fuzzy inference system, followed by the dynamics of a dynamic multi-criteria decision-making model the prioritization of 15 international rail transport companies for the Islamic Republic of Iran Railways was established. The results indicated that dealing with the problem of ranking international rail transport companies dynamically will achieve a more favorable outcome. The applicability of the presented method to benefit from the fuzzy inference system in the dynamic multi-criteria decision-making model is one of the other results.

This study could develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies. The proposed dynamic multi-criteria decision-making model includes several implications. Firstly, it provides a comprehensive and systematic approach to evaluate and rank international rail transport companies based on multiple criteria and factors. This approach can help decision-makers to make informed decisions and allocate resources effectively. Secondly, the use of the fuzzy inference system to estimate future data is a significant contribution to the field of transportation. This mathematical tool can handle uncertainty and imprecision in data, which is common in transportation systems. This can help decision-makers to anticipate future trends and plan accordingly. Moreover, the proposed model can be customized and applied to other realms and fields. The model can be adapted to the specific needs and requirements of each problem, making it a versatile and adaptable tool for decision-making. By using this model, decision-makers can take into account multiple criteria and factors, such as cost, time, and environmental impact, and evaluate different scenarios to find the most optimal solution.