## 1 Introduction
Noteworthy applications encompass the Analytic Hierarchy Process (AHP) [22; 26; 27; 28; 29], the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [28; 30], the Additive Ratio Assessment System (ARAS) [21; 30], VIsekrierijumska optimizacija i Kompromisno Resenje (VIKOR) [22], SWARA [31], Evaluation Based On Distance From Average Solution (EDAS) [23], Combinative Distance-based ASesment (CODAS) [23; 32], Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) [23; 29], Cooperation Platform for Research and Standards (COPRAS) [30], COmprehensive Distance-Based RAnking (COBRA) [26], among others, and their various combinations. The application of MCDM methods enables a comprehensive evaluation of potential solutions, highlighting their advantages, disadvantages, and limitations. In this paper, we integrate Fuzzy SWARA and ADAM methods. Subsequently, we provide an overview of relevant studies wherein these methods have been effectively employed. [PERSON] et al. [33] developed the SWARA method, but [PERSON] et al. [32] were the first to carry it out in a fuzzy environment. The method is used to determine the weight of a criteria based on the order of importance of each criterion, from the most important to the least important. Decision makers evaluate the criteria based on their knowledge, experience, and information available from the analyzed area. Based on the multiple applications of this method in different areas, it can be concluded that it can be effectively applied in the area of material handling technology selection, in which it has not been used so far, which represents a research gap that this paper addresses. Compared to other MCDM methods, the SWARA method's main advantages are that it is simple to use, the problem-solving algorithm is clear and simple for inexperienced users to understand, it takes a short time to implement, and it is just as effective for group decision making as it is for decision making by a single decision maker. The required number of comparisons (evaluations) is not large, being significantly less than in AHP or Analytic Network Process (ANP) methods. The method is very flexible, and consistency is achieved by ranking the criteria in decreasing order. Additionally, a predefined scale for comparing criteria is not required [34; 35]. The SWARA method is a useful tool for problem analysis and decision making. However, because of inadequate data, decision makers' evaluations of decision factors are frequently imprecise, unclear, and vague. This was the reason why the fuzzy extension of the SWARA method was used for this paper. [PERSON] et al. [36] applied the Fuzzy SWARA method to determine the weight of observed risks in the mining industry. [PERSON] et al. [37] solved the problem of the selection of personnel in their paper. They used the Delphi method to define their criteria, and the SWARA method was used to determine the weights of the criteria. [PERSON] et al. [38] investigated potential ways to reduce road transport emissions. To select an acceptable solution, they used multi-objective neural optimization MULTIMOORA and the SWARA method. [PERSON] and [PERSON] [34] used the SWARA method for ranking the key performance indicators (KPIs) in transport. In addition to the above, the SWARA method has found applications for solving problems in various areas, such as supplier selection [39],product design [40], the prioritization of sustainability indicators in energy systems [41], machine selection [42], landslide risk assessment [43], etc. The ADAM method, introduced by [PERSON] et al. [44], is a geometric MCDM approach that ranks alternatives based on the volumes of complex polyhedra in a three-dimensional coordinate system. The points in this system include the coordinate origin (O), reference points (R) indicating alternative values on the x-y plane, and weighted reference points (P) introducing criteria weights along the z-axis.