## 4 Methodology
### Fuzzy Rank Ordering Criteria Weighing Method  
In typical MCDM, decision-makers (DMs) offer precise numerical values for the information and the ratings and weights of criteria are quantified using crisp numbers [55]. Fuzzy logic deals with subjective judgment, imprecise information, and ambiguous opinions in the MCDM problem. The MCDM method typically consists of four distinct stages: formulation of alternatives and selection criteria, weighting criteria, evaluation of alternatives, and final aggregation and ranking [55]. Weight elicitation is a crucial component in multi-criteria decision analysis, as it can substantially impact the outcomes of multi-criteria-aiding procedures. Determining weights involves ranking the criteria and converting the resulting ranking into numerical values. Utilizing rankings to derive weights through formulas can be more dependable than explicitly giving weights to criteria [41]. DMs typically have greater confidence in determining the ranks of specific criteria and find it easier to reach an agreement on them than randomly selecting the weights of those criteria. The most renowned and extensively utilized strategy is the rank sum weight method. PWD's preferences alone define the criteria weights derived by the subjective weighting procedures. Fuzzy concepts are applied in gathering weights because human judgments, including preferences, are frequently imprecise and cannot be characterized by precise numerical values. The proposed methodology extends the crisp rank ordering criteria weighting methods. The methods based on rank order techniques consider imprecise information about rank. In the Fuzzy Rank Ordering Criteria Weighing Method, the weights calculated for each criterion are represented as triangular fuzzy numbers (TFNs). The input is produced as a list of prioritized 'n' fuzzy-ranked criteria, with weights and ranks inversely correlated. The criteria are assigned fuzzy ranks in ascending sequence, with the most essential criterion being given rank 1 and the least essential criterion is given rank n based on these steps [41]:  
Step 1. Ranking the criteria depends on their importance for each wheelchair user. \[w_{1}\geq\cdots w_{k}\geq\cdots\geq w_{n},\ k=1,2,\ldots,n \tag{1}\]  
Step 2. Weighting the criteria from their fuzzy ranks. Multi-criteria decision-making (MCDM) problems can be divided into two kinds. In classical MCDM problems, the information provided by the decision-makers (DMs) takes exact numerical values, and the ratings and the weights of criteria are measured in crisp numbers. In fuzzy multi-criteria decision-making (FMCDM) problems, as in our case, the ratings and the weights of criteria evaluated on incomplete information, imprecision, subjective judgment and vagueness are usually expressed by interval or triangle fuzzy numbers (three-dimensional values). The MCDM method typically consists of four distinct stages: formulation of alternatives and selection criteria, weighting criteria, evaluation of alternatives, and final aggregation and ranking [41]. Weight elicitation is a crucial component in multi-criteria decision analysis, as it can substantially impact the outcomes of multi-criteria-aiding procedures. Determining weights involves ranking the criteria and converting the resulting ranking into numerical values. Utilizing rankings to derive weights through formulas can be more dependable than explicitly giving weights to criteria [55]. Fuzzy concepts are applied in gathering weights because human judgments, including preferences, are frequently imprecise and cannot be characterized by precise numerical values. The proposed methodology extends the crisp rank ordering criteria weighting methods. The methods based on rank order techniques consider imprecise information about rank.