## 1 Introduction  
Mining operations, crucial to numerous economies worldwide, are undergoing a transformative phase, marked by heightened environmental concerns, technological advancements, and a growing emphasis on operational efficiency [1, 2]. These operations include a wide range of activities, from excavation and material extraction to transportation and processing. Within the complex structure of mining activities, transport systems are one of the key components, enabling the smooth movement of materials across large mining sites [3]. Material transportation is a critical aspect of mining operations, significantly influencing costs, efficiency, safety, and environmental impact [4, 5]. Considering that loading and transportation costs amount to 60% of the total operating costs, it is essential that these systems are efficient and reliable [6]. The environmental footprint of traditional diesel-powered transport methods necessitates research into sustainable alternatives such as electric and autonomous vehicles [7, 8]. Moreover, safety and risk management are paramount, with advanced technologies such as automation and real-time monitoring offering new opportunities for optimizing transport routes and enhancing decision-making [9]. Given the complex and variable conditions of mining environments, tailored solutions are essential, making continuous research in this area crucial for achieving greater sustainability, operational excellence, and economic viability in the mining industry. Various methods and models are used for the purposes of this research in the field of mining, which provide the possibility of dealing with the complex and changing conditions of the mining environment. One of the most widely applied mathematical approaches is the theory of fuzzy sets, which is suitable for the analysis of processes in which uncertainty, ambiguity, subjectivity, and indeterminacy prevail [10]. The application of this method enables the aforementioned problem to be successfully analyzed, and the analysis results reflect the previous expert experiences and results of experimental measurements in a good way. To assess the success of using this method, prior knowledge of the behavior of the analyzed systems and processes is necessary. By comparing the experience and experimental data with the analysis results of the fuzzy logic method, its verification is carried out. Below is an overview of works with the application of fuzzy logic in mining and similar topics. The largest number of studies is related to the field of mechanization in mining, where fuzzy logic was used to evaluate the performance of mining equipment under different operating conditions. The approach takes into account multiple criteria such as reliability, efficiency, and maintenance costs [10; 11; 12; 13; 14; 15; 16]. For example, the dependability of bucket wheel excavators, which are complex hierarchical systems, has been analyzed using fuzzy sets to synthesize information from the component level to the entire system, applying evident reasoning theory [17]. This mathematical approach has also found application in various parts of the production process in surface and underground mines, where processes such as loading, transportation, drilling, and blasting are adequately optimized [18; 19; 20; 21; 22]. The scientific literature also highlights the pervasive nature of risk in mining operations [23; 24; 25; 26; 27; 28], emphasizing the need for robust risk management strategies to mitigate the impact of potential failures, where risk assessment methodologies such as failure mode and effects analysis (FMEA) and risk priority number (RPN) calculations are used very effectively in combination with fuzzy logic [29]. New approaches for assessing maintenance support and integrating it into the dependability concept have been developed, with fuzzy algebra playing a key role in this process.