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Modern challenges such as the liberalization of the railway sector and growing demands for sustainability, high-quality services, and user satisfaction set new standards in railway operations. In this context, railway infrastructure managers (RIMs) play a crucial role in ensuring innovative approaches that will strengthen the position of railways in the market by enhancing efficiency and competitiveness. Evaluating their performance is essential for assessing the achieved objectives, and it is conducted through a wide range of key performance indicators (KPIs), which encompass various dimensions of operations. Monitoring and analyzing KPIs are crucial for improving service quality, achieving sustainability, and establishing a foundation for research and development of new strategies in the railway sector. This paper provides a detailed overview and evaluation of KPIs for RIMs. This paper creates a framework for RIM evaluation using various scientific methods, from identifying KPIs to applying complex analysis methods. A novel hybrid model, which integrates the fuzzy Delphi method for aggregating expert opinions on the KPIs’ importance, the extended fuzzy analytic hierarchy process (AHP) method for determining the relative weights of these KPIs, and the ADAM method for ranking RIMs, has been developed in this paper. This approach enables a detailed analysis and comparison of RIMs and their performances, providing the basis for informed decision-making and the development of new strategies within the railway sector. The analysis results provide insight into the current state of railway infrastructure and encourage further efforts to improve the railway sector by identifying key areas for enhancement. The main contributions of the research include a detailed overview of KPIs for RIMs and the development of a hybrid multi-criteria decision making (MCDM) model. The hybrid model represents a significant step in RIM performance analysis, providing a basis for future research in this area. The model is universal and, as such, represents a valuable contribution to MCDM theory.

The European railway sector doesn't represent a single, generic type of organizational structure. The Directives from the 1990s offer the possibility for national interpretation giving a wide range of organizational structures for European railways. Different organizational structures of railways are present in the European railway market, and all of them are aligned with the national belief of the country - how to manage the railway. This paper focuses on Multi-Criteria Decision Making (MCDM) methods for ranking and selecting organizational structures of railways. In decision-making, we deal with different types of uncertainty and inaccuracy. For this purpose, we need to use some specific tools. In this paper, we use triangular fuzzy numbers to quantify linguistic data. To overcome the complexity of the decision-making problem, we propose the fuzzy Additive Ratio ASsessment (ARAS) method, which also includes linguistic variables and a group approach to decision-making. With the proposed methodology an evaluation of the alternatives to the organization structure of the railway company in Bosnia and Herzegovina is considered. Separation of railway transport from infrastructure management is ranked as the best organizational structure for this company.

The operational transport strategies of a state are developed as a combination of train organization systems and network usage systems. Due to the central importance of these operational transport strategies, special attention is paid to analyzing their functioning and the conditions under which different combinations of train organization systems and network usage become feasible and commercially viable. This study proposes a hybrid fuzzy approach to multi-criteria decision-making methods for evaluating and assessing train organization systems and network usage in Bosnia and Herzegovina. For this purpose, an extended Fuzzy Analytic Hierarchy Process (E-FAHP) was applied to determine the relative significance of each criterion, followed by ranking train systems using the Fuzzy Additive Ratio Assessment Method (Fuzzy ARAS). To validate the efficacy of the proposed framework, a case study was conducted using a real-world example. The findings demonstrate tangible potential for practical implementation.