1. Introduction
Road traffic accidents are one of the leading causes of death and serious injuries for people all across the world. Based on a report by the World Health Organization, the road traffic death rate is from 8.3 in highincome countries, to 27.5 per 100,000 inhabitants in low and middleincome countries. In total, there are around 1.35 million deaths and 50 million injured on the roads each year [
1]. Many problems in transportation, such as traffic congestion, road blockage, and road accidents, can be solved to a certain extent by the digitization of roads and vehicles [
2]; however, the impact of the human factor is still of crucial importance as well. In the literature, there are three main categories of factors that contribute to road traffic accident occurrence: human factors, vehicle factors, and the factor related to road design and construction. Human factors are by far the most represented cause of accidents. Around 90 to 95% of all traffic accidents involve human error [
3,
4,
5]. Based on this, it is evident that adequate educational programs for drivers can contribute to improved traffic safety. In addition, particular attention should be placed on the recruitment procedures for professional drivers having in mind their constant and widespread presence on the roads.
A professional driver represents a driver who drives a transport vehicle as a paid employee. In this study, we are focused on road transport, and the case study is related to bus drivers. However, the proposed methodology is general and can be implemented for other types of drivers, such as truck drivers, train drivers, sea captains, or airline pilots, as well as in general cases, for any personnel selection problem.
Professional drivers, depending on the type of driving vehicle, are responsible for goods of high value that are transported, and even more importantly, for the lives of people that travel by different means of public transport. This job is very demanding, both from the physical and mental standpoint [
6]. These are the reasons why particular attention should be placed on the selection procedure, to employ candidates that are capable of responding to all of the challenges of this work. It is evident that various criteria influence the assessment procedure of candidates and one of the aims of this paper is to investigate the literature and systemize the criteria used in the process of professional driver selection.
The main aim of this paper is to propose a methodological framework for solving the problem of professional driver selection. Having in mind, on the one hand, that certain criteria should be minimized or maximized in decisionmaking depending on their nature, and on the other hand, that the candidates can be considered as alternatives, it can be concluded that this is a typical multicriteria decisionmaking (MCDM) problem. There are numerous MCDM techniques in the literature [
7,
8,
9]; however, to contribute both to the professional and scientific fields, we propose an extension of a relatively new MCDM method: An alternative ranking order method accounting for twostep normalization—AROMAN [
10,
11]. Because certain criteria in the process of professional driver selection are hard to describe numerically, we introduce fuzzy logic in the model. The main motive for applying the AROMAN method is that it is a very new MCDM method and we intend to further test its applicability. Aside from this, our crucial goal is to test this method in a fuzzy environment. Therefore, the main contribution of this study can be structured as follows: (i) We identified the criteria for the selection of professional drivers by a comprehensive literature review, (ii) We measured the relevance ranks of the set criteria by interviewing the experts from the field and by implementing Fuller’s triangle method, (iii) We implemented the AROMAN method in the fuzzy environment for the first time in the literature, (iv) We proposed an extension of the AROMAN method by integrating the relevance ranks of the set criteria obtained by Fuller’s triangle method with the calculation of alternatives ranks.
The rest of the paper is structured as follows:
Section 2 is a review of the literature to identify the criteria for professional driver selection.
Section 3 explains the methodology of the research. To illustrate the applicability of the proposed model, in
Section 4 there is an illustrative case study. Finally,
Section 5 represents a conclusion.
2. Literature Review
This section investigates the current knowledge in the field of professional drivers. The main research source for this study was the Web of Science (WoS) database. There are two subsections. The first is related to the review of different topics considered in this field and the second is devoted to the identification of criteria used for the professional driver evaluation.
2.1. Literature on Professional Drivers
In the last decade, there have been numerous papers addressing the issue of professional drivers. Here, we will mention just a few to demonstrate the diversity of considered problems in the field. For instance, Zaranka et al. [
12] evaluated the factors affecting the behavior of road users and investigated the impact of fatigue on road users. Maslać et al. [
13] compared the behaviors of professional and nonprofessional drivers in the Republic of Serbia. Rosso et al. [
14] conducted a crosssectional questionnaire survey to investigate obesity among professional drivers in Italy. Chen et al. [
15] investigated the difference between professional and nonprofessional drivers in terms of the effectiveness of the compensatory strategy adopted by older drivers. Wu et al. [
16] carried out research related to the effectiveness of ecodriving training for male professional and nonprofessional drivers.
Öz et al. [
17] considered professional and nonprofessional drivers’ stress reactions and risky driving. Nordfaern et al. [
18] investigated safety attitudes, behavior, anxiety, and perceived control among professional and nonprofessional drivers. SerranoFernández et al. [
19] addressed the most important predictive variables for sleep quality in professional drivers. Chen et al. [
20] conducted a driving simulator study regarding the safety of professional drivers in an aging society. Meng et al. [
21] investigated driving fatigue in professional taxi and truck drivers in Beijing. Han and Jianyou [
22] tackled driver behavior and traffic accident involvement among professional urban bus drivers in China. HernándezRodríguez et al. [
23] estimated psychosocial risk and job satisfaction in professional drivers. SerranoFernández et al. [
24] considered variables that predict attitudes toward safety regulations in professional drivers. Llamazares et al. [
25] investigated commuting accidents of Spanish professional drivers when the occupational risk exceeds the workplace. The related research papers are summarized in
Table 1.
As can be noticed from this part of the literature review, various forms of studies tackling professional drivers have been conducted by researchers around the globe. However, the research gap in the literature is that there is a lack of studies considering the professional driver evaluation and selection problem. Having the stated in mind, this paper aims to address the professional driver evaluation and selection issue using a multicriteria decisionmaking approach combined with fuzzy logic. More concretely, this paper applies the recently developed AROMAN method, coupled with the fuzzy logic to effectively evaluate and select the most appropriate professional driver. As a starting point to perform this procedure, the evaluation criteria should be set. The next subchapter is devoted to this task, where the criteria are identified by the review of current publications.
2.2. Criteria for Professional Drivers Evaluation
In this subsection, a summarized overview of the considered criteria for professional driver evaluation is offered, along with the applied methodology in the related papers (
Table 2). Professional drivers need to maintain their attention on many traffic circumstances while driving. Cvahte Ojsteršek and Topolšek [
26] analyzed the influence of drivers’ visual and cognitive attention on their perception of changes in the traffic environment. Further, Milošević and Gajić [
27] considered how different situations in traffic impacts the perception of road signs.
Milosevic [
28] examined the fatigue resistance in a group of city bus drivers, by measuring heart rate by electrocardio recorder before and after driving. It is interesting to notice that mental fatigue can cause negative effects on physical performance as well [
29].
Drivers’ reaction time is related to the amount of time needed to process important information and act in emergencies. Poliak et al. [
30] evaluated the impact of age on the reaction time of professional drivers. Čulík et al. [
31] investigated if gender, practice, or alcohol significantly affected the reaction time of drivers.
Anstey et al. [
32] proposed a model to measure the capacity to drive safely based on assessing visual functions. Lacherez et al. [
33] examined an association between indices of driving safety and lowlevel changes in visual function.
Papers that deal with the selection procedure of professional drivers by using a multicriteria decisionmaking approach are very rare in the literature. One of them is by Chen et al. [
34] where speed estimation is taken as an evaluation criterion. The research by ČubranićDobrodolac et al. [
35] confirms that a level of speed perception capabilities is related to the occurrence of road traffic accidents.
In the literature, an interdependence between physical fitness and driving skills is proven [
36]. Since professional drivers often drive even during the night, resulting in a lack of sleep, this segment is of particular importance in the selection procedure [
37].
Inexperience is one of the strongest predictors of crashes [
39]. Therefore, work experience is often taken as an evaluation criterion in the staff recruitment process [
38], as well as risk assessment [
40]. Driver’s improper driving behavior is often related to poor risk assessment [
40,
41].
Examples of driver aggression are related to driving at excessive speed, intimidation of other road users, improper following, improper lane changing and passing, and similar. It is proven that aggressiveness positively correlates with road accident occurrence [
42,
44].
Aside from aggression, similar behavior represents impulsiveness. There is evidence in the literature that elevated impulsiveness is associated with other forms of inappropriate behavior in traffic, such as drinkdriving, drugdriving offenses, using a mobile phone behind the wheel, etc. [
43,
50].
A higher level of selfassessment of driving ability can be found in drivers with a lower level of involvement in crashes [
45]. A comprehensive review of methods for measuring subjective driving ability can be found in [
46]. Another ability that contributes to safe driving relates to space assessment capability [
47].
Intelligence is an innate ability that is very often considered in the recruitment procedure, in the field of transportation, and many others [
48,
49]. Zaranka et al. [
12] introduce a social component in the recruitment procedure of professional drivers, where the first things considered are morality, social fit, and interpersonal skills [
49,
51].
3. Methods
After an extensive literature review to identify criteria for professional driver evaluation, further research methodology can be structured into two parts. The first aim of reducing the number of identified criteria to simplify the calculation process in the second part relates to the ranking of candidates. In the first part, we apply two methods, DEMATEL and the Fuller triangle method. Interdependence between the criteria is calculated by DEMATEL, while the level of importance of each criterion to the decisionmaking process is determined by the Fuller triangle method. The second part of the methodology is related to the proposal of the Fuzzy–AROMAN–Fuller approach for ranking the alternatives. The research structure is presented in
Figure 1.
3.1. Determination of Interdependence between the Criteria by the DEMATEL Method
The DEMATEL method can be structured into four main steps. They relate to generating the directrelation matrix by interviewing the experts, normalization of the directrelation matrix, calculating the totalrelation matrix, and producing a causal diagram. In the following text, the procedure is explained in more detail.
The directrelation matrix is created by using a comparison scale:
0 = No influence;
1 = Low influence;
2 = Medium influence;
3 = High influence;
4 = Very high influence.
An expert answers the questions considering the degree of influence of one criterion over another. Let
${a}_{ij}$ denotes a pairwise comparison score between two criteria. If there are
n criteria, and all the comparisons are obtained, the directrelation matrix
$A={\left[{a}_{ij}\right]}_{nxn}$ can be formed. If there are more experts
${A}^{1}$,
${A}^{2}$, …,
${A}^{m}$, the final directrelation matrix can be generated by Equation (1) [
52].
The normalized directrelation matrix
X, X $={\left[{x}_{ij}\right]}_{nxn}$ and
$0\le {x}_{ij}\le 1$, can be calculated by Equations (2) and (3). It should be noted that all diagonal elements are equal to zero [
52].
The totalrelation matrix
T can be obtained by applying Equation (4). Here, the matrix
I indicates the identity matrix [
52].
To interpret the results, the important variables are
D and
R. They are calculated by Equations (6) and (7) [
52].
A causal diagram can be obtained by mapping the pairs (D + R, D − R). The first dimension gives information about the impact of a particular criterion over others, while the second dimension describes the nature of this impact, i.e., is a criterion in the cause (positive values) or effect group (negative values).
3.2. Determination of the Criteria Relevance by the Fuller Triangle Method
To reduce the number of criteria identified by the literature review, we will determine their relevance and rank them according to the method named the Fuller triangle method [
53,
54,
55]. The Fuller method is in the group of the subjective weighting methods, such as the Analytic Hierarchy Process—AHP [
56], Best–Worst Method—BWM [
57], Full Consistency Method—FUCOM [
58], or Stepwise Weight Assessment Ratio Analysis—SWARA [
59]. The procedure of the Fuller triangle method is explained in the following text.
Step 1. The Fuller method starts with forming a triangular structure as shown in Figure 2. The first two rows relate to the pairwise comparison of Criterion 1 with all other criteria. Accordingly, in the first row, there are n − 1 columns with Criterion 1 and the same number of all other criteria from Criterion 2 to n. A decision maker should choose which of the considered criteria in each pair is more important than the other and mark it. The same procedure should be performed for all other comparisons; however, each of the subsequent two rows is shorter by one column, and at the end, there is just a comparison of one pair, between Criterion n − 1 and n. The number of all pairs being compared is equal to N, calculated by Equation (8), where n is the total number of compared criteria.
Step 2. After all comparisons are carried out, it can be considered that each criterion that “win” as the more important one in the pairwise comparison receives one point. If a decision is made that they are of equal significance, the criteria achieve half of a point each. The points awarded to criteria should be summed up per each criterion, and the sums represent their relevance rank.
Step 3. In the third step, the relevance ranks (${w}_{j}$) should be normalized according to Equation (9) [53]. ${v}_{j}$ represents the number of preferences, i.e., the number of points each criterion received, and in the denominator is the total number of all preferences.
Step 4. If there is a criterion that did not receive any points, in this case, in the previous formula, the numerator should be increased by 1 to avoid the relevance rank being equal to zero. Then, the relevance ranks should be calculated by Equation (10).
If the evaluation of relevance ranks is carried out by more than one expert, an arithmetic mean value of all input values should be calculated. To aggregate opinions from more experts, other methods can be used as well, such as geometric mean [
60] or centroid [
61]. However, the arithmetic mean is the most commonly used.
An important issue considering subjective methods, such is the Fuller triangle method, is measuring the reliability of collected answers. For example, in the AHP method, there is a wellknown approach where the rate of inconsistency is calculated, and it should be lower than 0.1 to conclude satisfactory reliability. However, this approach cannot be applied in the case of the Fuller triangle method, and in addition, by reviewing the literature, we did not find any convenient approach that could be implemented here. This was an inspiration for the authors to propose a new technique to assess the reliability of experts’ opinions.
The procedure implies a second round of interviewing the experts. Without knowing the results of the first round where they gave opinions about the pairwise comparisons of criteria importance, they were asked to give additional assessments. They were told to imagine the scale from 0 to 100% and to determine the percentage importance of each criterion for the recruitment process of selecting the professional driver. This should be carried out in the way that all 14 assessments give the sum of 100%. Finally, the results of the second round should be compared with the first round to conclude about the reliability of answers. If we denote the answers from the second round by
${p}_{j}$, and previously we marked the obtained weight in the first round by
${w}_{j}$, then the rate of inconsistency (
RI) can be calculated as explained in Equation (11).
3.3. Ranking Alternatives Using a Hybridized FuzzyAROMANFULLER Approach
As previously mentioned, the AROMAN method is for the first time implemented in a fuzzy environment in this paper. Therefore, it is useful to provide some preliminaries on fuzzy arithmetic.
3.3.1. Preliminaries on Fuzzy Arithmetic
In the following text, we briefly present some basic definitions of fuzzy sets and numbers [
62].
Definition 1. A fuzzy set $\stackrel{~}{A}$ in a universe of discourse X is characterized by a membership function ${\mu}_{\stackrel{~}{A}}$(x) which associates with each element x in X a real number in the interval $\left[0,1\right]$. The function value ${\mu}_{\stackrel{~}{A}}$(x) represents the grade of membership of x in $\stackrel{~}{A}$ [62]. Definition 2. A fuzzy set $\stackrel{~}{A}$ of the universe of discourse X is convex if and only if for all ${x}_{1}$, ${x}_{2}$ in X,where $\in \left[0,1\right]$ [62]. Definition 3. A fuzzy set $\stackrel{~}{A}$ of the universe of discourse X is called a normal fuzzy set implying that $\exists $ ${x}_{i}$ $\in $ X, ${\mu}_{\stackrel{~}{A}}$ (${x}_{i}$) = 1 [62]. Definition 4. A fuzzy number is a fuzzy subset in the universe of discourse X that is both convex and normal. An example of a triangular fuzzy number is given in Figure 3. Definition 5. The αcut of fuzzy number $\stackrel{~}{n}$ is definedwhere α$\in \left[0,1\right]$ [62]. ${\stackrel{~}{n}}^{\alpha}$ is a nonempty bounded closed interval contained in X and it can be denoted by
${\stackrel{~}{n}}^{\alpha}$ =
$\left[{\stackrel{~}{n}}_{\iota}^{\alpha},{\stackrel{~}{n}}_{u}^{\alpha}\right],$ where
${\stackrel{~}{n}}_{\iota}^{\alpha}$ and
${\stackrel{~}{n}}_{u}^{\alpha}$ are the lower and upper bounds of the closed interval, respectively.
Figure 4 shows a fuzzy number
$\stackrel{~}{n}$ with αcuts, where
From
Figure 4 we can see that if
${\mathsf{\alpha}}_{2}\ge {\mathsf{\alpha}}_{1}$, then
${\stackrel{~}{n}}_{\iota}^{\alpha 2}\ge {\stackrel{~}{n}}_{\iota}^{\alpha 1}\mathrm{a}\mathrm{n}\mathrm{d}{\stackrel{~}{n}}_{u}^{\alpha 1}\ge {\stackrel{~}{n}}_{u}^{\alpha 2}$.
Definition 6. A triangular fuzzy number $\stackrel{~}{n}$ can be defined by a triplet (${n}_{1}$, ${n}_{2}$, ${n}_{3}$) shown in Figure 3. The membership function ${\mu}_{\stackrel{~}{n}}$($x)$ is defined as [62]: Definition 7. If ${\stackrel{~}{n}}^{}$ is a fuzzy number and ${\stackrel{~}{n}}_{l}^{\alpha}>0\hspace{1em}for\hspace{1em}\alpha \in \left[0,1\right]$, then $\stackrel{~}{n}$ is called a positive fuzzy number.
Given any two positive fuzzy numbers
$\stackrel{~}{m},$ $\stackrel{~}{n}$ and a positive real number
r, the αcut of two fuzzy numbers are
${\stackrel{~}{m}}^{\alpha}$ =
$\left[{m}_{l}^{\alpha},{m}_{u}^{\alpha}\right]$ and
${\stackrel{~}{n}}^{\alpha}$ = [
${n}_{l}^{\alpha},{n}_{u}^{\alpha}$] (
$\mathsf{\alpha}\in $ [0,1]), respectively. According to the interval of confidence, some main operations of positive fuzzy numbers
$\stackrel{~}{m}\mathrm{a}\mathrm{n}\mathrm{d}\stackrel{~}{n}$ can be expressed as follows [
62]:
where
r is a constant.
3.3.2. Fuzzy–AROMAN–Fuller Approach
An extension of the AROMAN method [
10,
11] to the fuzzy environment is proposed in this part. The method is very convenient for solving MCDM problems where more experts are involved in the decisionmaking process. The procedure will be explained in steps.
A fuzzy MCDM problem can be presented in matrix format as:
where
${\stackrel{~}{x}}_{ij}$ are linguistic variables.
To rate the qualitative criteria, the inputs are linguistic variables. These linguistic variables can be expressed as triangular fuzzy numbers. The scale is offered in
Table 3.
If there are
K experts that evaluate the alternatives based on set criteria, then the ratings can be calculated as:
In the further procedure, the normalization of data should be carried out. The AROMAN method implies two types of normalization, as explained in Steps 2 and 3.
Step 2. Normalization No. 1.
Step 3. Normalization No. 2.
As it is generally known, in MCDM problems certain criteria should be minimized, also known as cost criteria, and the others should be maximized, often named as benefit criteria. The normalization procedure in Steps 2 and 3 should be applied for both criteria types (min and max).
The aggregated normalization is obtained by Equation (25).
where
${\stackrel{~}{t}}_{ij}^{norm}$ denotes the aggregated averaged normalization.
β is a weighting factor for each type of normalization varying from 0 to 1.
In this step, the aggregated normalized decisionmaking (DM) matrix should be multiplied by the criteria weights to obtain a weighted DM matrix. Here, the weights of criteria are determined by the previously explained Fuller triangle method.
Further procedure relates to a summation of the normalized weighted values separately for the criteria type min (
${\stackrel{~}{L}}_{i}$) and the type max (
${\stackrel{~}{A}}_{i}$).
Step 7. Raise the obtained ${\stackrel{~}{L}}_{i}$ and ${\stackrel{~}{A}}_{i}$ values to the degree of λ.
where
$\lambda $ represents the coefficient degree of the criterion type. Parameter
$\lambda $ can be determined in different ways; however, we propose using the weights obtained by the Fuller triangle method. If we mark the weights of the criteria of min type by
${w}_{j}^{min}$, then the parameter
$\lambda $ can be obtained by Equation (31).
To obtain the final ranking of alternatives (
${R}_{i}$), the difference between the values
${{\stackrel{~}{A}}_{i}}^{^}$ and
${{\stackrel{~}{L}}_{i}}^{^}$ should be calculated and the final ranking equation applied.
4. Case Study
To demonstrate the applicability of the proposed methodology, we provide an illustrative numerical example. Let us suppose that the task to be solved is to select the most appropriate bus driver from the three candidates who applied for the job. The candidates can be considered the alternatives in the MCDM process, and we denote them as A1, A2, and A3. The criteria that are used for the evaluation of candidates are identified in the literature. According to the model, the number of criteria should be reduced to seven. This will be completed by interviewing the experts from the field and implementing the Fuller triangle method which gives the importance rank for each considered criterion. However, additional information about the criteria and their interdependence can be obtained by the DEMATEL method.
Since both the DEMATEL and the Fuller triangle method belong to the group of subjective methods, the first task in their implementation is to identify and interview the appropriate experts. In this case study, we collected the answers from three experts. The first is from the field of Traffic psychology and the other two are experts in Road traffic safety. All experts have more than 15 years of professional experience. They also possess Ph.D. degrees.
4.1. The Results of the DEMATEL Method
As explained in the methodology section, there are four steps in the DEMATEL implementation.
Step 1. The experts assessed the interdependence between the criteria in the pairwise comparisons and gave marks from 0 to 4 depending on the type of relation. Their answers are presented in Table A1, Table A3 and Table A5 in Appendix A, Appendix B and Appendix C. Based on these answers, we formed the directrelation matrix, as shown in Table 4.
Step 2. The normalized directrelation matrix X is calculated according to Equations (2) and (3). The resulting matrix is in Table 5.
Step 3. We calculated the totalrelation matrix (Table 6) by using MATLAB software. It is applied also to create a causal diagram in the fourth step.
Step 4. A causal diagram is formed based on D and R values calculated by Equations (6) and (7) and obtained values are in Table 7. Finally, a causal diagram is shown in Figure 5.
As can be noticed from
Figure 5, criteria C3, C5, C11, and C12 belong to the effect group, while the others are in the cause group. Since we can conclude about a relatively low interdependence between the criteria, this is a good prerequisite to conducting the Fuller triangle method.
4.2. The Results of the Fuller Triangle Method
The procedure is carried out according to the previous methodological explanation.
Step 1. We formed a triangular structure where 14 criteria were involved. Such a form was offered to the experts and they were asked to make pairwise comparisons of criteria in the case of the bus driver selection problem. Their answers are presented in Table A2, Table A4 and Table A6 in Appendix A, Appendix B and Appendix C. The fields marked with green color are their choices. Step 2. Further procedure implies counting the collected answers. In Table 8, Columns 3, 5, and 7, are the point that each criterion received by experts 1, 2, and 3, respectively. Step 3. The relevance ranks (${w}_{j}$) are calculated for each criterion and each expert and presented in Table 8, Columns 4, 6, and 8. Step 4. The final relevance ranks are obtained. They are presented in the final column of Table 8, as well as in Figure 6, where they are aligned in descending order to easier notice the first seven that will be used in the further calculations.
Table 8.
The relevance ranks of criteria obtained by the Fuller triangle method.
Table 8.
The relevance ranks of criteria obtained by the Fuller triangle method.
No.  Criteria  Number of Preferences by Expert 1  ${\mathit{w}}_{\mathit{j}}$ Based on Expert 1
 Number of Preferences by Expert 2  ${\mathit{w}}_{\mathit{j}}$ Based on Expert 2
 Number of Preferences by Expert 3  ${\mathit{w}}_{\mathit{j}}$ Based on Expert 3
 Final ${\mathit{w}}_{\mathit{j}}$ 

1.  Attention  9  0.099  8  0.088  8  0.088  0.092 
2.  Fatigue resistance  1  0.011  4  0.044  6  0.066  0.040 
3.  Reaction time  9  0.099  9  0.099  10  0.110  0.103 
4.  Visual abilities  10  0.110  10  0.110  11  0.121  0.114 
5.  Speed estimation  8  0.088  8  0.088  8  0.088  0.088 
6.  Physical fitness  8  0.088  7  0.077  8  0.088  0.084 
7.  Driving experience  4  0.044  4  0.044  6  0.066  0.051 
8.  Risk assessment  9  0.099  10  0.110  10  0.110  0.106 
9.  Impulsiveness  9  0.099  8  0.088  8  0.088  0.092 
10.  Aggressiveness  6  0.066  7  0.077  6  0.066  0.070 
11.  Selfassessment of driving ability  6  0.066  4  0.044  2  0.022  0.044 
12.  Space capabilities  2  0.022  2  0.022  1  0.011  0.018 
13.  Intelligence  6  0.066  6  0.066  3  0.033  0.055 
14.  Morality  4  0.044  4  0.044  4  0.044  0.044 
Total  91  1  91  1  91  1  1 
To check the reliability of the obtained results, we interviewed the experts in the second round to collect information about the percentage distribution of criteria importance. The results are shown in
Table 9. As it can be concluded, the rate of inconsistency is below 0.1 (
RI = 0.07), which means that reliability is satisfactory.
4.3. The Results of a Hybridized Fuzzy–AROMAN–Fuller Approach
As we mentioned, the subject of the case study is a busoperating company that needs to hire a bus driver. Three potential candidates are marked by A1, A2, and A3, the interviewed experts by E1, E2, and E3, and set evaluation criteria from C1 to C14, where only seven of them are considered in this part of the model. The structure of the considered MCDM problem is shown in
Figure 7.
The implementation of the Fuzzy–AROMAN–Fuller approach for solving the mentioned problem is presented in steps, as previously explained in the methodological section.
Step 1. Let us suppose that the experts use linguistic variables to evaluate the alternatives and that their answers form the initial decisionmaking matrix as shown in Table 10. The linguistic inputs are converted to fuzzy numbers following the rules presented in Table 3. The fuzzy decision matrix that is averaged by Equation (23) is shown in Table 11.
Step 2. Normalization No. 1 is performed and the obtained results are shown in Table 12.
Step 3. Normalization No. 2 is complete and the obtained results are in Table 13.
Step 4. The aggregated normalization is calculated by Equation (26), where we considered the parameter β to be 0.5. The results are in Table 14.
Step 5. Next, the weighted fuzzy decisionmaking matrix is formed. We used the weights obtained by the Fuller triangle method; however, since the number of criteria is reduced from 14 to 7, we arranged the sum of the remaining 7 weights to be equal to 1. The resulting weighted matrix is shown in Table 15.
Step 6. In this step, the summation of the weighted aggregated normalized fuzzy decisionmaking matrix should be completed per the criteria type. In our case, the min type criteria are C3 and C9, while the max type criteria are C4, C8, C1, C5, and C6.
Step 7. The sums from Step 6 should be raised to the degree of λ, which is in our case, according to Equation (32) equal to 0.29. The obtained values are in Table 16.
Step 8. In the final step, we calculate the final ranking by Equation (33). As shown in Table 17, the results of implemented method indicate that the best candidate is A2, followed by A3 and A1.
5. Conclusions
The problem of personnel selection is very complex, bearing in mind that multiple criteria should be considered in the candidate evaluation process. The task is even more complicated when it comes to demanding jobs, such is the job of a professional driver. There is often a need to manipulate uncertain or imprecise data. In this paper, we demonstrated how a hybridized Fuzzy–AROMAN–Fuller approach can be successfully used to solve the considered problem.
This research resulted in several contributions. First of all, by reviewing the literature from the field of traffic psychology, road safety, and personnel selection, we identified the criteria that should be used in the process of professional driver selection. Further, we interviewed the experts to determine the relevance ranks of the set criteria. We interviewed three eminent experts from the field of road traffic safety and traffic psychology; however, a direction for future research can be to include more experts in the research and to compare the results. Finally, for the first time in the literature, we applied the AROMAN method in a fuzzy environment. We further couple it with the Fuller triangle method. By solving a numerical example, we demonstrated the applicability of the proposed model. Additional paths for future research can be directed toward comparing the obtained results with some other MCDM approaches. For example, the criteria weights can be determined by AHP and coupled with the AROMAN method, or the final ranking of alternatives can be performed by some other MCDM method and be compared with the AROMAN.
Although we demonstrated the proposed model on the example of a professional driver selection problem, this model is general and can be applied to many other problems. These problems can relate to personnel selection in other fields; however, a hybridized Fuzzy–AROMAN–Fuller approach can be implemented for solving any other MCDM problem.
Author Contributions
Conceptualization, M.Č.D. and S.J.; methodology, M.Č.D., S.J. and S.B.; software, D.B.; validation, M.Č.D., S.J. and S.B.; formal analysis, M.Č.D. and S.B.; investigation, S.J. and S.B.; resources, M.Č.D.; data curation, M.Č.D. and D.B.; writing—original draft preparation, M.Č.D. and S.J.; writing—review and editing, S.B. and D.B.; visualization, S.J.; supervision, M.Č.D.; project administration, D.B. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported in part by the Research Program SGS_2023_017.
Data Availability Statement
All research data are presented in this paper, in the main part and appendices as well.
Acknowledgments
The authors are grateful for the valuable comments of the Editors, and the two anonymous reviewers, who helped to improve the manuscript greatly.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A. Answers from Expert 1
Table A1.
Answers related to the DEMATEL method from Expert 1.
Table A1.
Answers related to the DEMATEL method from Expert 1.
 C1  C2  C3  C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14 

C1  0  2  1  0  1  0  1  1  1  1  1  1  1  1 
C2  2  0  2  1  1  1  1  1  1  1  2  1  1  0 
C3  1  1  0  1  1  1  0  0  0  0  0  1  1  0 
C4  0  1  3  0  1  1  1  1  0  0  1  1  0  0 
C5  1  1  2  1  0  0  1  2  1  1  2  1  0  0 
C6  0  1  1  1  0  0  0  0  0  0  1  0  0  0 
C7  0  1  0  1  2  0  0  2  1  1  2  1  0  0 
C8  1  1  0  1  2  0  1  0  2  2  2  2  1  1 
C9  1  1  0  0  1  2  1  2  0  3  1  0  0  1 
C10  1  1  0  0  1  0  1  2  3  0  1  0  0  1 
C11  1  0  1  1  2  1  0  2  1  1  0  1  1  1 
C12  1  1  1  1  1  0  1  2  0  0  1  0  2  0 
C13  1  1  1  0  1  0  0  1  0  0  1  2  0  0 
C14  0  0  0  0  0  0  0  1  1  1  1  0  0  0 
Table A2.
Answers related to the Fuller triangle method from Expert 1.
Table A2.
Answers related to the Fuller triangle method from Expert 1.
C1  C1  C1  C1  C1  C1  C1  C1  C1  C1  C1  C1  C1 
C2  C3  C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14 
C2  C2  C2  C2  C2  C2  C2  C2  C2  C2  C2  C2  
C3  C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14  
C3  C3  C3  C3  C3  C3  C3  C3  C3  C3  C3   
C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14   
C4  C4  C4  C4  C4  C4  C4  C4  C4  C4    
C5  C6  C7  C8  C9  C10  C11  C12  C13  C14    
C5  C5  C5  C5  C5  C5  C5  C5  C5     
C6  C7  C8  C9  C10  C11  C12  C13  C14     
C6  C6  C6  C6  C6  C6  C6  C6      
C7  C8  C9  C10  C11  C12  C13  C14      
C7  C7  C7  C7  C7  C7  C7       
C8  C9  C10  C11  C12  C13  C14       
C8  C8  C8  C8  C8  C8        
C9  C10  C11  C12  C13  C14        
C9  C9  C9  C9  C9         
C10  C11  C12  C13  C14         
C10  C10  C10  C10          
C11  C12  C13  C14          
C11  C11  C11           
C12  C13  C14           
C12  C12            
C13  C14            
C13             
C14             
Appendix B. Answers from Expert 2
Table A3.
Answers related to the DEMATEL method from Expert 2.
Table A3.
Answers related to the DEMATEL method from Expert 2.
 C1  C2  C3  C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14 

C1  0  1  1  0  1  0  0  1  0  0  0  2  0  0 
C2  1  0  2  1  1  0  0  1  1  1  0  0  0  0 
C3  0  0  0  0  1  0  0  0  0  0  0  1  1  0 
C4  0  0  2  0  1  1  1  1  0  0  1  0  0  0 
C5  1  2  3  0  0  0  0  2  1  1  3  1  1  0 
C6  0  1  1  0  1  0  0  0  1  1  1  0  0  0 
C7  1  0  0  1  0  0  0  2  1  0  1  1  0  0 
C8  1  0  1  1  3  0  0  0  2  2  2  2  0  0 
C9  0  0  0  0  1  0  0  1  0  4  0  0  0  1 
C10  0  1  0  0  1  0  0  2  4  0  1  0  0  1 
C11  0  1  1  1  2  1  0  2  1  1  0  1  1  1 
C12  0  1  1  1  1  0  1  2  0  0  1  0  2  0 
C13  1  1  0  0  1  0  0  1  0  0  1  2  0  0 
C14  0  0  0  0  0  0  0  0  1  1  1  0  0  0 
Table A4.
Answers related to the Fuller triangle method from Expert 2.
Table A4.
Answers related to the Fuller triangle method from Expert 2.
C1  C1  C1  C1  C1  C1  C1  C1  C1  C1  C1  C1  C1 
C2  C3  C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14 
C2  C2  C2  C2  C2  C2  C2  C2  C2  C2  C2  C2  
C3  C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14  
C3  C3  C3  C3  C3  C3  C3  C3  C3  C3  C3   
C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14   
C4  C4  C4  C4  C4  C4  C4  C4  C4  C4    
C5  C6  C7  C8  C9  C10  C11  C12  C13  C14    
C5  C5  C5  C5  C5  C5  C5  C5  C5     
C6  C7  C8  C9  C10  C11  C12  C13  C14     
C6  C6  C6  C6  C6  C6  C6  C6      
C7  C8  C9  C10  C11  C12  C13  C14      
C7  C7  C7  C7  C7  C7  C7       
C8  C9  C10  C11  C12  C13  C14       
C8  C8  C8  C8  C8  C8        
C9  C10  C11  C12  C13  C14        
C9  C9  C9  C9  C9         
C10  C11  C12  C13  C14         
C10  C10  C10  C10          
C11  C12  C13  C14          
C11  C11  C11           
C12  C13  C14           
C12  C12            
C13  C14            
C13             
C14             
Appendix C. Answers from Expert 3
Table A5.
Answers related to the DEMATEL method from Expert 3.
Table A5.
Answers related to the DEMATEL method from Expert 3.
 C1  C2  C3  C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14 

C1  0  1  1  0  1  0  1  1  1  1  1  1  1  1 
C2  2  0  3  0  0  1  0  0  0  1  2  1  0  0 
C3  1  1  0  0  1  1  0  0  0  0  0  1  0  0 
C4  0  1  3  0  1  1  0  1  0  0  1  1  0  0 
C5  1  1  2  1  0  0  0  1  0  0  2  1  1  0 
C6  0  1  2  1  1  0  0  1  0  0  1  0  0  0 
C7  1  1  1  0  0  0  0  1  1  1  2  1  0  0 
C8  1  1  0  1  2  0  0  0  1  3  2  1  0  0 
C9  1  1  0  0  1  0  1  2  0  1  1  0  0  0 
C10  1  1  0  0  1  0  1  2  1  0  1  0  0  0 
C11  1  1  1  1  1  0  0  1  0  0  0  1  0  0 
C12  0  0  2  1  1  0  0  1  0  0  1  0  1  0 
C13  0  0  1  0  1  0  0  0  0  0  1  0  0  0 
C14  1  1  0  0  0  0  0  1  2  2  2  0  0  0 
Table A6.
Answers related to the Fuller triangle method from Expert 3.
Table A6.
Answers related to the Fuller triangle method from Expert 3.
C1  C1  C1  C1  C1  C1  C1  C1  C1  C1  C1  C1  C1 
C2  C3  C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14 
C2  C2  C2  C2  C2  C2  C2  C2  C2  C2  C2  C2  
C3  C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14  
C3  C3  C3  C3  C3  C3  C3  C3  C3  C3  C3   
C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14   
C4  C4  C4  C4  C4  C4  C4  C4  C4  C4    
C5  C6  C7  C8  C9  C10  C11  C12  C13  C14    
C5  C5  C5  C5  C5  C5  C5  C5  C5     
C6  C7  C8  C9  C10  C11  C12  C13  C14     
C6  C6  C6  C6  C6  C6  C6  C6      
C7  C8  C9  C10  C11  C12  C13  C14      
C7  C7  C7  C7  C7  C7  C7       
C8  C9  C10  C11  C12  C13  C14       
C8  C8  C8  C8  C8  C8        
C9  C10  C11  C12  C13  C14        
C9  C9  C9  C9  C9         
C10  C11  C12  C13  C14         
C10  C10  C10  C10          
C11  C12  C13  C14          
C11  C11  C11           
C12  C13  C14           
C12  C12            
C13  C14            
C13             
C14             
References
 World Health Organization. Global Status Report on Road Safety 2018; World Health Organization: Geneva, Switzerland, 2018.
 Debnath, P. A QGISBased Road Network Analysis for Sustainable Road Network Infrastructure: An Application to the Cachar District in Assam, India. Infrastructures 2022, 7, 114. [Google Scholar] [CrossRef]
 Cai, Q. Cause Analysis of Traffic Accidents on Urban Roads Based on an Improved Association Rule Mining Algorithm. IEEE Access 2020, 8, 75607–75615. [Google Scholar] [CrossRef]
 ČubranićDobrodolac, M.; Švadlenka, L.; Čičević, S.; Dobrodolac, M. Modelling Driver Propensity for Traffic Accidents: A Comparison of Multiple Regression Analysis and Fuzzy Approach. Int. J. Inj. Contr. Saf. Promot. 2019, 27, 156–167. [Google Scholar] [CrossRef] [PubMed]
 ČubranićDobrodolac, M.; Lipovac, K.; Čičević, S.; Antić, B. A Model for Traffic Accidents Prediction Based on Driver Personality Traits Assessment. Promet—TrafficTransport. 2017, 29, 631–642. [Google Scholar] [CrossRef] [Green Version]
 Crizzle, A.M.; Bigelow, P.; Adams, D.; Gooderham, S.; Myers, A.M.; Thiffault, P. Health and Wellness of LongHaul Truck and Bus Drivers: A Systematic Literature Review and Directions for Future Research. J. Transp. Health 2017, 7, 90–109. [Google Scholar] [CrossRef]
 Stojčić, M.; Zavadskas, E.K.; Pamučar, D.; Stević, Ž.; Mardani, A. Application of MCDM Methods in Sustainability Engineering: A Literature Review 2008–2018. Symmetry 2019, 11, 350. [Google Scholar] [CrossRef] [Green Version]
 Hezam, I.M.; Rahman, K.; Alshamrani, A.; Božanić, D. Geometric Aggregation Operators for Solving Multicriteria Group DecisionMaking Problems Based on Complex Pythagorean Fuzzy Sets. Symmetry 2023, 15, 826. [Google Scholar] [CrossRef]
 Svadlenka, L.; Simic, V.; Dobrodolac, M.; Lazarevic, D.; Todorovic, G. Picture Fuzzy DecisionMaking Approach for Sustainable LastMile Delivery. IEEE Access 2020, 8, 209393–209414. [Google Scholar] [CrossRef]
 Boskovic, S.; Svadlenka, L.; Jovcic, S.; Dobrodolac, M.; Simic, V.; Bacanin, N. An Alternative Ranking Order Method Accounting for TwoStep Normalization (AROMAN); A Case Study of the Electric Vehicle Selection Problem. IEEE Access 2023, 11, 39496–39507. [Google Scholar] [CrossRef]
 Bošković, S.; Švadlenka, L.; Dobrodolac, M.; Jovčić, S.; Zanne, M. An Extended AROMAN Method for Cargo Bike Delivery Concept Selection. Decis. Mak. Adv. 2023, 1, 1–9. [Google Scholar] [CrossRef]
 Zaranka, J.; Pečeliunas, R.; Žuraulis, V. A Road SafetyBased Selection Methodology for Professional Drivers: Behaviour and Accident Rate Analysis. Int. J. Environ. Res. Public Health 2021, 18, 12487. [Google Scholar] [CrossRef]
 Maslać, M.; Antić, B.; Lipovac, K.; Pešić, D.; Milutinović, N. Behaviours of Drivers in Serbia: NonProfessional versus Professional Drivers. Transp. Res. Part F Traffic Psychol. Behav. 2018, 52, 101–111. [Google Scholar] [CrossRef]
 Rosso, G.L.; Perotto, M.; Feola, M.; Bruno, G.; Caramella, M. Investigating Obesity among Professional Drivers: The High Risk Professional Driver Study. Am. J. Ind. Med. 2015, 58, 212–219. [Google Scholar] [CrossRef]
 Chen, T.; Sze, N.N.; Newnam, S.; Bai, L. Effectiveness of the Compensatory Strategy Adopted by Older Drivers: Difference between Professional and NonProfessional Drivers. Transp. Res. Part F Traffic Psychol. Behav. 2021, 77, 168–180. [Google Scholar] [CrossRef]
 Wu, Y.; Zhao, X.; Rong, J.; Zhang, Y. The Effectiveness of EcoDriving Training for Male Professional and NonProfessional Drivers. Transp. Res. Part D Transp. Environ. 2018, 59, 121–133. [Google Scholar] [CrossRef]
 Öz, B.; Özkan, T.; Lajunen, T. Professional and NonProfessional Drivers’ Stress Reactions and Risky Driving. Transp. Res. Part F Traffic Psychol. Behav. 2010, 13, 32–40. [Google Scholar] [CrossRef]
 Nordfaern, T.; Jorgensen, S.H.; Rundmo, T. Safety Attitudes, Behaviour, Anxiety and Perceived Control among Professional and NonProfessional Drivers. J. Risk Res. 2012, 15, 875–896. [Google Scholar] [CrossRef]
 SerranoFernández, M.J.; BoadaGrau, J.; RobertSentís, L.; VigilColet, A. Predictive Variables for Sleep Quality in Professional Drivers. An. Psicol. 2021, 37, 393–401. [Google Scholar] [CrossRef]
 Chen, T.; Sze, N.N.; Bai, L. Safety of Professional Drivers in an Ageing Society—A Driving Simulator Study. Transp. Res. Part F Traffic Psychol. Behav. 2019, 67, 101–112. [Google Scholar] [CrossRef]
 Meng, F.; Li, S.; Cao, L.; Li, M.; Peng, Q.; Wang, C.; Zhang, W. Driving Fatigue in Professional Drivers: A Survey of Truck and Taxi Drivers. Traffic Inj. Prev. 2015, 16, 474–483. [Google Scholar] [CrossRef]
 Han, W.; Zhao, J. Driver Behaviour and Traffic Accident Involvement among Professional Urban Bus Drivers in China. Transp. Res. Part F Traffic Psychol. Behav. 2020, 74, 184–197. [Google Scholar] [CrossRef]
 HernándezRodríguez, V.; MaesoGonzález, E.; GutiérrezBedmar, M.; GarcíaRodríguez, A. Psychosocial Risk and Job Satisfaction in Professional Drivers. Front. Psychol. 2022, 13, 5660. [Google Scholar] [CrossRef] [PubMed]
 SerranoFernández, M.J.; TàpiaCaballero, P.; BoadaGrau, J.; ArayaCastillo, L. Variables That Predict Attitudes Toward Safety Regulations in Professional Drivers. J. Transp. Health 2020, 19, 100967. [Google Scholar] [CrossRef]
 Llamazares, J.; Useche, S.A.; Montoro, L.; Alonso, F. Commuting Accidents of Spanish Professional Drivers: When Occupational Risk Exceeds the Workplace. Int. J. Occup. Saf. Ergon. 2021, 27, 754–762. [Google Scholar] [CrossRef]
 Cvahte Ojsteršek, T.; Topolšek, D. Influence of Drivers’ Visual and Cognitive Attention on Their Perception of Changes in the Traffic Environment. Eur. Transp. Res. Rev. 2019, 11, 45. [Google Scholar] [CrossRef]
 Milošević, S.; Gajić, R. Presentation Factors and Driver Characteristics Affecting RoadSign Registration. Ergonomics 2007, 29, 807–815. [Google Scholar] [CrossRef]
 Milosevic, S. Drivers’ Fatigue Studies. Ergonomics 1997, 40, 381–389. [Google Scholar] [CrossRef]
 Brown, D.M.Y.; Farias Zuniga, A.; Mulla, D.M.; Mendonca, D.; Keir, P.J.; Bray, S.R. Investigating the Effects of Mental Fatigue on Resistance Exercise Performance. Int. J. Environ. Res. Public Health 2021, 18, 6794. [Google Scholar] [CrossRef]
 Poliak, M.; Svabova, L.; Benus, J.; Demirci, E. Driver Response Time and Age Impact on the Reaction Time of Drivers: A Driving Simulator Study among ProfessionalTruck Drivers. Mathematics 2022, 10, 1489. [Google Scholar] [CrossRef]
 Čulík, K.; Kalašová, A.; Štefancová, V. Evaluation of Driver’s Reaction Time Measured in Driving Simulator. Sensors 2022, 22, 3542. [Google Scholar] [CrossRef]
 Anstey, K.J.; Horswill, M.S.; Wood, J.M.; Hatherly, C. The Role of Cognitive and Visual Abilities as Predictors in the Multifactorial Model of Driving Safety. Accid. Anal. Prev. 2012, 45, 766–774. [Google Scholar] [CrossRef] [Green Version]
 Lacherez, P.; Au, S.; Wood, J.M. Visual Motion Perception Predicts Driving Hazard Perception Ability. Acta Ophthalmol. 2014, 92, 88–93. [Google Scholar] [CrossRef] [Green Version]
 Chen, X.M.; Wei, Z.H.; Gao, L. Professional Driver Suitability Evaluation. Procedia Eng. 2011, 15, 5222–5226. [Google Scholar] [CrossRef]
 ČubranićDobrodolac, M.; Švadlenka, L.; Čičević, S.; Trifunović, A.; Dobrodolac, M. A Bee Colony Optimization (BCO) and Type2 Fuzzy Approach to Measuring the Impact of Speed Perception on Motor Vehicle Crash Involvement. Soft Comput. 2021, 26, 4463–4486. [Google Scholar] [CrossRef]
 Caragata, G.E.; Tuokko, H.; Damini, A. Fit to Drive: A Pilot Study to Improve the Physical Fitness of Older Drivers. Act. Adapt. Aging 2009, 33, 240–255. [Google Scholar] [CrossRef]
 Gilson, N.D.; Mielke, G.I.; Coombes, J.S.; Feter, N.; Smith, E.; Duncan, M.J.; Wallis, G.; Brown, W.J. VO2peak and 24Hour Sleep, Sedentary Behavior, and Physical Activity in Australian Truck Drivers. Scand. J. Med. Sci. Sports 2021, 31, 1574–1578. [Google Scholar] [CrossRef]
 Ku Khalif, K.M.N.; Gegov, A.; Abu Bakar, A.S. Hybrid Fuzzy MCDM Model for ZNumbers Using Intuitive Vectorial Centroid. J. Intell. Fuzzy Syst. 2017, 33, 791–805. [Google Scholar] [CrossRef] [Green Version]
 Mueller, A.S.; Trick, L.M. Driving in Fog: The Effects of Driving Experience and Visibility on Speed Compensation and Hazard Avoidance. Accid. Anal. Prev. 2012, 48, 472–479. [Google Scholar] [CrossRef]
 Wang, T.; Chen, B.; Chen, Y.; Deng, S.; Chen, J. Traffic Risk Assessment Based on Warning Data. J. Adv. Transp. 2022, 2022, 11. [Google Scholar] [CrossRef]
 AlGarawi, N.; Dalhat, M.A.; Aga, O. Assessing the Road Traffic Crashes among Novice Female Drivers in Saudi Arabia. Sustainability 2021, 13, 8613. [Google Scholar] [CrossRef]
 CubranicDobrodolac, M.; Svadlenka, L.; Markovic, G.Z.; Dobrodolac, M. A Decision Support Model for Transportation Companies to Examine Driver Behavior. IEEE Trans. Eng. Manag. 2021. [Google Scholar] [CrossRef]
 Smorti, M.; Guarnieri, S. Do Aggressive Driving and Negative Emotional Driving Mediate the Link between Impulsiveness and Risky Driving among Young Italian Drivers? J. Soc. Psychol. 2016, 156, 669–673. [Google Scholar] [CrossRef] [PubMed]
 Rodriguez Gonzalez, A.B.; Wilby, M.R.; Vinagre Diaz, J.J.; Sanchez Avila, C. Modeling and Detecting Aggressiveness from Driving Signals. IEEE Trans. Intell. Transp. Syst. 2014, 15, 1419–1428. [Google Scholar] [CrossRef]
 Tronsmoen, T. Associations between SelfAssessment of Driving Ability, Driver Training and Crash Involvement among Young Drivers. Transp. Res. Part F Traffic Psychol. Behav. 2008, 11, 334–346. [Google Scholar] [CrossRef]
 Sundström, A. SelfAssessment of Driving Skill—A Review from a Measurement Perspective. Transp. Res. Part F Traffic Psychol. Behav. 2008, 11, 1–9. [Google Scholar] [CrossRef]
 ČubranićDobrodolac, M.; Švadlenka, L.; Čičević, S.; Trifunović, A.; Dobrodolac, M. Using the Interval Type2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents. Mathematics 2020, 8, 1548. [Google Scholar] [CrossRef]
 Petrović, I.; Petrović, J. Personality Traits in Selection of Military, Civil and Sports’ Pilots: HybridizedIT2FSMCDM Approach. Int. J. Traffic Transp. Eng. 2021, 12, 1–20. [Google Scholar] [CrossRef]
 Li, Y.M.; Lai, C.Y.; Kao, C.P. Building a Qualitative Recruitment System via SVM with MCDM Approach. Appl. Intell. 2011, 35, 75–88. [Google Scholar] [CrossRef]
 ČubranićDobrodolac, M.; Čičević, S.; Dobrodolac, M.; Nešić, M. The Risks Associated with Using a Mobile Phone by Young Drivers. Transport 2013, 28, 381–388. [Google Scholar] [CrossRef]
 Gottwald, D.; Jovčić, S.; Lejsková, P. MultiCriteria DecisionMaking Approach in Personnel Selection Problem—A Case Study at the University of Pardubice. Econ. Comput. Econ. Cybern. Stud. Res. 2022, 56, 149–164. [Google Scholar] [CrossRef]
 Wu, W.W.; Lee, Y.T. Developing Global Managers’ Competencies Using the Fuzzy DEMATEL Method. Expert Syst. Appl. 2007, 32, 499–507. [Google Scholar] [CrossRef]
 Stopka, O.; Stopková, M.; Ľupták, V.; Krile, S. Application of the Chosen MultiCriteria DecisionMaking Methods to Identify the Autonomous Train System Supplier. Transp. Probl. 2020, 15, 45–57. [Google Scholar] [CrossRef]
 Agarski, B.; Budak, I.; Kosec, B.; Hodolic, J. An Approach to MultiCriteria Environmental Evaluation with Multiple Weight Assignment. Environ. Model. Assess. 2012, 17, 255–266. [Google Scholar] [CrossRef]
 Agarski, B.; Hadzistevic, M.; Budak, I.; Moraca, S.; Vukelic, D. Comparison of Approaches to Weighting of Multiple Criteria for Selecting Equipment to Optimize Performance and Safety. Int. J. Occup. Saf. Ergon. 2017, 25, 228–240. [Google Scholar] [CrossRef]
 Dobrodolac, M.; Lazarević, D.; Švadlenka, L.; Živanović, M. A Study on the Competitive Strategy of the Universal Postal Service Provider. Technol. Anal. Strateg. Manag. 2016, 28, 935–949. [Google Scholar] [CrossRef] [Green Version]
 Alshamrani, A.; Majumder, P.; Das, A.; Hezam, I.M.; Božanić, D. An Integrated BWMTOPSISI Approach to Determine the Ranking of Alternatives and Application of Sustainability Analysis of Renewable Energy. Axioms 2023, 12, 159. [Google Scholar] [CrossRef]
 Pamučar, D.; Lukovac, V.; Božanić, D.; Komazec, N. MultiCriteria FucomMairca Model for the Evaluation of Level Crossings: Case Study in the Republic of Serbia. Oper. Res. Eng. Sci. Theory Appl. 2018, 1, 108–129. [Google Scholar] [CrossRef] [Green Version]
 Karabasevic, D.; Stanujkic, D.; Urosevic, S. The MCDM Model for Personnel Selection Based on SWARA and ARAS Methods. Manag. J. Theory Pract. Manag. 2015, 20, 43–52. [Google Scholar] [CrossRef]
 Mariani, F.; Ciommi, M. Aggregating Composite Indicators through the Geometric Mean: A Penalization Approach. Computation 2022, 10, 64. [Google Scholar] [CrossRef]
 Lazarević, D.; Dobrodolac, M.; Švadlenka, L.; Stanivuković, B. A Model for Business Performance Improvement: A Case of the Postal Company. J. Bus. Econ. Manag. 2020, 21, 564–592. [Google Scholar] [CrossRef] [Green Version]
 Chen, C.T. Extensions of the TOPSIS for Group DecisionMaking under Fuzzy Environment. Fuzzy Sets Syst. 2000, 114, 1–9. [Google Scholar] [CrossRef]
Figure 1.
Research structure.
Figure 1.
Research structure.
Figure 2.
Illustration of starting procedure in Fuller triangle method.
Figure 2.
Illustration of starting procedure in Fuller triangle method.
Figure 3.
A triangular fuzzy number $\stackrel{~}{n}$.
Figure 3.
A triangular fuzzy number $\stackrel{~}{n}$.
Figure 4.
Fuzzy number $\stackrel{~}{n}$ with $\alpha $ cuts.
Figure 4.
Fuzzy number $\stackrel{~}{n}$ with $\alpha $ cuts.
Figure 5.
A causal diagram.
Figure 5.
A causal diagram.
Figure 6.
Descending order of the relevance ranks of criteria.
Figure 6.
Descending order of the relevance ranks of criteria.
Figure 7.
The structure of the MCDM problem.
Figure 7.
The structure of the MCDM problem.
Table 1.
Studies related to professional drivers.
Table 1.
Studies related to professional drivers.
Year  Authors  Considered Problem 

2010  Öz, Özkan and Lajunen [17]  Professional and nonprofessional drivers’ stress reactions and risky driving 
2012  Nordfaern, Jorgensen and Rundmo [18]  Safety attitudes, behavior, anxiety, and perceived control among professional and nonprofessional drivers 
2015  Rosso, Perotto, Feola, Bruno and Caramella [14]  Obesity among professional drivers 
2015  Meng, Li, Cao, Li, Peng, Wang and Zhang [21]  Driving fatigue among professional taxi and truck drivers 
2018  Maslać, Antić, Lipovac, Pešić and Milutinović [13]  Comparison of the professional and nonprofessional drivers considering rules violations 
2018  Wu, Zhao, Rong and Zhang [16]  Effectiveness of ecodriving training for male professional and nonprofessional drivers 
2019  Chen, Sze and Bai [20]  Safety of professional drivers in an ageing society 
2020  Han and Zhao [22]  Driver behavior and traffic accident involvement among professional urban bus drivers in China 
2020  SerranoFernández, TàpiaCaballero, BoadaGrau and ArayaCastillo [24]  Variables that predict attitudes toward safety regulations in professional drivers 
2021  Zaranka, Pečeliunas and Žuraulis [12]  Factors affecting the behavior of road users 
2021  SerranoFernández, BoadaGrau, RobertSentís and VigilColet [19]  Predictive variables for sleep quality in professional drivers 
2021  Chen, Sze, Newnam and Bai [15]  Difference between professional and nonprofessional drivers in terms of the effectiveness 
2021  Llamazares, Useche, Montoro and Alonso [25]  Commuting accidents of professional drivers when the occupational risk exceeds the workplace 
2022  HernándezRodríguez, MaesoGonzález, GutiérrezBedmar and GarcíaRodríguez [23]  Psychosocial risk and job satisfaction in professional drivers 
Table 2.
Criteria for professional driver evaluation.
Table 2.
Criteria for professional driver evaluation.
No.  Criteria  Authors  Used Methodology 

1.  Attention   
Cvahte Ojsteršek and Topolšek [ 26]  
  
Statistical analysis  
Statistical analysis

2.  Fatigue resistance   
 
Brown, Farias Zuniga, Mulla, Mendonca, Keir and Bray [ 29]
  
Statistical analysis  
Statistical analysis

3.  Reaction time   
Poliak, Svabova, Benus and Demirci [ 30]  
Čulík, Kalašová and Štefancová [ 31]
  
Statistical analysis  
Statistical analysis

4.  Visual abilities   
Anstey, Horswill, Wood and Hatherly [ 32]  
Lacherez, Au and Wood [ 33]
  
Statistical analysis  
Statistical analysis

5.  Speed estimation   
 
ČubranićDobrodolac, Švadlenka, Čičević, Trifunović and Dobrodolac [ 35]
  
Fuzzy AHP  
Fuzzy inference system

6.  Physical fitness   
Caragata, Tuokko and Damini [ 36]  
Gilson, Mielke, Coombes, Feter, Smith, Duncan, Wallis and Brown [ 37]
  
Statistical analysis  
Statistical analysis

7.  Driving experience   
Ku Khalif, Gegov and Abu Bakar [ 38]  
  
Fuzzy TOPSIS  
Statistical analysis

8.  Risk assessment   
Wang, Chen, Chen, Deng, Chen [ 40]  
AlGarawi, Dalhat and Aga [ 41]
  
Statistical analysis  
Statistical analysis

9.  Impulsiveness   
CubranicDobrodolac, Svadlenka, Markovic and Dobrodolac [ 42]  
Smorti and Guarnieri [ 43]
  
Fuzzy inference system  
Statistical analysis

10.  Aggressiveness   
CubranicDobrodolac, Svadlenka, Markovic and Dobrodolac [ 42]  
Rodriguez Gonzalez, Wilby, Vinagre Diaz and Sanchez Avila [ 44]
  
Fuzzy inference system  
Statistical analysis

11.  Selfassessment of driving ability   
 
  
Statistical analysis  
Statistical analysis

12.  Space capabilities   
ČubranićDobrodolac, Švadlenka, Čičević, Trifunović and Dobrodolac [ 47]
  
Fuzzy inference system

13.  Intelligence   
Petrović and Petrović [ 48]  
  
Fuzzy TOPSIS  
TOPSIS

14.  Morality   
Zaranka, Pečeliunas and Žuraulis [ 12]  
  
Statistical analysis  
TOPSIS

Table 3.
Linguistic variables for the ratings of criteria.
Table 3.
Linguistic variables for the ratings of criteria.
Linguistic Variable  Fuzzy Number 

Very low (VL)  (0,0,1) 
Low (L)  (0,1,3) 
Mediumlow (ML)  (1,3,5) 
Medium (M)  (3,5,7) 
Mediumhigh (MH)  (5,7,9) 
High (H)  (7,9,10) 
Very High (VH)  (9,10,10) 
Table 4.
The directrelation matrix.
Table 4.
The directrelation matrix.
 C1  C2  C3  C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14 

C1  0.00  1.33  1.00  0.00  1.00  0.00  0.67  1.00  0.67  0.67  0.67  1.33  0.67  0.67 
C2  1.67  0.00  2.33  0.67  0.67  0.67  0.33  0.67  0.67  1.00  1.33  0.67  0.33  0.00 
C3  0.67  0.67  0.00  0.33  1.00  0.67  0.00  0.00  0.00  0.00  0.00  1.00  0.67  0.00 
C4  0.00  0.67  2.67  0.00  1.00  1.00  0.67  1.00  0.00  0.00  1.00  0.67  0.00  0.00 
C5  1.00  1.33  2.33  0.67  0.00  0.00  0.33  1.67  0.67  0.67  2.33  1.00  0.67  0.00 
C6  0.00  1.00  1.33  0.67  0.67  0.00  0.00  0.33  0.33  0.33  1.00  0.00  0.00  0.00 
C7  0.67  0.67  0.33  0.67  0.67  0.00  0.00  1.67  1.00  0.67  1.67  1.00  0.00  0.00 
C8  1.00  0.67  0.33  1.00  2.33  0.00  0.33  0.00  1.67  2.33  2.00  1.67  0.33  0.33 
C9  0.67  0.67  0.00  0.00  1.00  0.67  0.67  1.67  0.00  2.67  0.67  0.00  0.00  0.67 
C10  0.67  1.00  0.00  0.00  1.00  0.00  0.67  2.00  2.67  0.00  1.00  0.00  0.00  0.67 
C11  0.67  0.67  1.00  1.00  1.67  0.67  0.00  1.67  0.67  0.67  0.00  1.00  0.67  0.67 
C12  0.33  0.67  1.33  1.00  1.00  0.00  0.67  1.67  0.00  0.00  1.00  0.00  1.67  0.00 
C13  0.67  0.67  0.67  0.00  1.00  0.00  0.00  0.67  0.00  0.00  1.00  1.33  0.00  0.00 
C14  0.33  0.33  0.00  0.00  0.00  0.00  0.00  0.67  1.33  1.33  1.33  0.00  0.00  0.00 
Table 5.
The normalized directrelation matrix.
Table 5.
The normalized directrelation matrix.
 C1  C2  C3  C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14 

C1  0.00  0.10  0.07  0.00  0.07  0.00  0.05  0.07  0.05  0.05  0.05  0.10  0.05  0.05 
C2  0.12  0.00  0.17  0.05  0.05  0.05  0.02  0.05  0.05  0.07  0.10  0.05  0.02  0.00 
C3  0.05  0.05  0.00  0.02  0.07  0.05  0.00  0.00  0.00  0.00  0.00  0.07  0.05  0.00 
C4  0.00  0.05  0.19  0.00  0.07  0.07  0.05  0.07  0.00  0.00  0.07  0.05  0.00  0.00 
C5  0.07  0.10  0.17  0.05  0.00  0.00  0.02  0.12  0.05  0.05  0.17  0.07  0.05  0.00 
C6  0.00  0.07  0.10  0.05  0.05  0.00  0.00  0.02  0.02  0.02  0.07  0.00  0.00  0.00 
C7  0.05  0.05  0.02  0.05  0.05  0.00  0.00  0.12  0.07  0.05  0.12  0.07  0.00  0.00 
C8  0.07  0.05  0.02  0.07  0.17  0.00  0.02  0.00  0.12  0.17  0.14  0.12  0.02  0.02 
C9  0.05  0.05  0.00  0.00  0.07  0.05  0.05  0.12  0.00  0.19  0.05  0.00  0.00  0.05 
C10  0.05  0.07  0.00  0.00  0.07  0.00  0.05  0.14  0.19  0.00  0.07  0.00  0.00  0.05 
C11  0.05  0.05  0.07  0.07  0.12  0.05  0.00  0.12  0.05  0.05  0.00  0.07  0.05  0.05 
C12  0.02  0.05  0.10  0.07  0.07  0.00  0.05  0.12  0.00  0.00  0.07  0.00  0.12  0.00 
C13  0.05  0.05  0.05  0.00  0.07  0.00  0.00  0.05  0.00  0.00  0.07  0.10  0.00  0.00 
C14  0.02  0.02  0.00  0.00  0.00  0.00  0.00  0.05  0.10  0.10  0.10  0.00  0.00  0.00 
Table 6.
The totalrelation matrix.
Table 6.
The totalrelation matrix.
 C1  C2  C3  C4  C5  C6  C7  C8  C9  C10  C11  C12  C13  C14 

C1  0.11  0.21  0.22  0.08  0.23  0.04  0.10  0.24  0.16  0.18  0.22  0.22  0.12  0.09 
C2  0.23  0.14  0.33  0.13  0.23  0.10  0.08  0.23  0.16  0.19  0.26  0.18  0.10  0.05 
C3  0.10  0.11  0.09  0.06  0.15  0.07  0.03  0.08  0.05  0.05  0.09  0.13  0.09  0.02 
C4  0.09  0.15  0.32  0.07  0.21  0.11  0.08  0.20  0.08  0.09  0.21  0.16  0.07  0.03 
C5  0.21  0.25  0.36  0.15  0.23  0.07  0.09  0.33  0.19  0.21  0.37  0.24  0.15  0.05 
C6  0.07  0.14  0.19  0.09  0.14  0.03  0.03  0.12  0.08  0.09  0.16  0.07  0.04  0.02 
C7  0.16  0.17  0.18  0.13  0.22  0.05  0.06  0.30  0.19  0.19  0.29  0.20  0.08  0.05 
C8  0.23  0.23  0.26  0.19  0.41  0.07  0.11  0.28  0.30  0.35  0.39  0.29  0.13  0.09 
C9  0.16  0.18  0.15  0.08  0.24  0.09  0.11  0.30  0.16  0.33  0.23  0.12  0.06  0.10 
C10  0.17  0.20  0.16  0.09  0.26  0.05  0.11  0.34  0.33  0.18  0.26  0.13  0.07  0.10 
C11  0.17  0.19  0.25  0.16  0.30  0.10  0.06  0.30  0.18  0.19  0.20  0.21  0.13  0.09 
C12  0.13  0.16  0.25  0.15  0.23  0.05  0.09  0.27  0.10  0.11  0.23  0.13  0.19  0.03 
C13  0.12  0.13  0.16  0.06  0.18  0.03  0.03  0.16  0.07  0.08  0.18  0.18  0.06  0.02 
C14  0.09  0.10  0.08  0.04  0.11  0.03  0.04  0.16  0.18  0.19  0.19  0.07  0.04  0.03 
Table 7.
D and R values.
Criterion  D Values  R Values  D + R  D − R 

C1  2.2332  2.0404  4.2736  0.1928 
C2  2.3986  2.3421  4.7407  0.0565 
C3  1.1113  3.0015  4.1129  −1.8902 
C4  1.8706  1.4894  3.3600  0.3813 
C5  2.8875  3.1333  6.0208  −0.2458 
C6  1.2819  0.8855  2.1674  0.3964 
C7  2.2486  1.0195  3.2681  1.2292 
C8  3.3333  3.3073  6.6406  0.0259 
C9  2.3082  2.2282  4.5364  0.0800 
C10  2.4422  2.4269  4.8691  0.0153 
C11  2.5329  3.2905  5.8234  −0.7577 
C12  2.1374  2.3323  4.4697  −0.1949 
C13  1.4515  1.3153  2.7668  0.1362 
C14  1.3436  0.7686  2.1122  0.5750 
Table 9.
Calculation of the rate of inconsistency.
Table 9.
Calculation of the rate of inconsistency.
Criteria  ${\mathit{w}}_{\mathit{j}}$  Expert 1 [%]  Expert 2 [%]  Expert 3 [%]  Average $\mathbf{Assessment}\u2014{\mathit{p}}_{\mathit{j}}$  $\left{\mathit{w}}_{\mathit{j}\mathit{*}100}{\mathit{p}}_{\mathit{j}}\right$  ${\mathit{R}\mathit{I}}_{\mathit{j}}$ 

C1  0.092  12  10  13  11.667  1.777  0.0178 
C2  0.040  2  1  3  2.000  0.901  0.0090 
C3  0.103  10  10  8  9.333  0.557  0.0056 
C4  0.114  10  12  11  11.000  0.011  0.0001 
C5  0.088  9  9  10  9.333  0.542  0.0054 
C6  0.084  8  9  9  8.667  0.125  0.0012 
C7  0.051  5  4  5  4.667  0.271  0.0027 
C8  0.106  9  10  9  9.333  0.557  0.0056 
C9  0.092  9  10  10  9.667  0.223  0.0022 
C10  0.070  6  6  8  6.667  0.073  0.0007 
C11  0.044  6  7  5  6.000  0.593  0.0059 
C12  0.018  3  2  2  2.333  0.136  0.0014 
C13  0.055  7  6  4  5.667  0.927  0.0093 
C14  0.044  4  4  3  3.667  0.729  0.0073 
      RI = 0.0742 
Table 10.
The ratings of candidates.
Table 10.
The ratings of candidates.
Criteria  Candidates  Experts   

E1  E2  E3 

C4  A1  H  M  M 
 A2  VH  H  H 
 A3  H  H  MH 
C8  A1  M  MH  M 
 A2  MH  H  H 
 A3  ML  M  M 
C3  A1  H  MH  MH 
 A2  L  VL  L 
 A3  M  ML  ML 
C1  A1  H  MH  M 
 A2  M  H  H 
 A3  VH  H  H 
C9  A1  M  M  M 
 A2  MH  M  M 
 A3  H  MH  MH 
C5  A1  MH  M  M 
 A2  H  MH  H 
 A3  VH  H  MH 
C6  A1  M  ML  ML 
 A2  M  H  MH 
 A3  MH  M  MH 
Table 11.
The fuzzy decision matrix.
Table 11.
The fuzzy decision matrix.
 C4  C8  C3  C1  C9  C5  C6 

A1  (4.33, 6.33, 8)  (3.67, 5.67, 7.67)  (5.67, 7.67, 9.33)  (5, 7, 8.67)  (3, 5, 7)  (3.67, 5.67, 7.67)  (1.67, 3.67, 5.67) 
A2  (7.67, 9.33, 10)  (6.33, 8.33, 9.67)  (0, 0.67, 2.33)  (5.67, 7.67, 9)  (3.67, 5.67, 7.67)  (6.33, 8.33, 9.67)  (5, 7, 8.67) 
A3  (6.33, 8.33, 9.67)  (2.33, 4.33, 6.33)  (1.67, 3.67, 5.67)  (7.67, 9.33, 10)  (5.67, 7.67, 9.33)  (7, 8.67, 9.67)  (4.33, 6.33, 8.33) 
Table 12.
Normalization No. 1 of the fuzzy decision matrix.
Table 12.
Normalization No. 1 of the fuzzy decision matrix.
 C4  C8  C3  C1  C9  C5  C6 

A1  (0, 0.35, 0.65)  (0.18, 0.45, 0.73)  (0.61, 0.82, 1)  (0, 0.4, 0.73)  (0, 0.32, 0.63)  (0, 0.33, 0.67)  (0, 0.28, 0.57) 
A2  (0.59, 0.88, 1)  (0.54, 0.82, 1)  (0, 0.07, 0.25)  (0.13, 0.53, 0.8)  (0.11, 0.42, 0.74)  (0.44, 0.78, 1)  (0.48, 0.76, 1) 
A3  (0.35, 0.71, 0.94)  (0, 0.27, 0.55)  (0.18, 0.39, 0.61)  (0.53, 0.87, 1)  (0.42, 0.74, 1)  (0.56, 0.83, 1)  (0.38, 0.67, 0.95) 
Table 13.
Normalization No. 2 of the fuzzy decision matrix.
Table 13.
Normalization No. 2 of the fuzzy decision matrix.
 C4  C8  C3  C1  C9  C5  C6 

A1  (0.27, 0.45, 0.74)  (0.26, 0.52, 0.99)  (0.51, 0.90, 1.58)  (0.31, 0.50, 0.81)  (0.21, 0.46, 0.95)  (0.23, 0.43, 0.76)  (0.13, 0.36, 0.83) 
A2  (0.48, 0.67, 0.92)  (0.45, 0.76, 1.26)  (0, 0.08, 0.39)  (0.35, 0.55, 0.84)  (0.26, 0.53, 1.04)  (0.40, 0.63, 0.95)  (0.38, 0.69, 1.27) 
A3  (0.39, 0.59, 0.89)  (0.17, 0.39, 0.82)  (0.14, 0.43, 0.96)  (0.48, 0.67, 0.93)  (0.41, 0.71, 1.26)  (0.47, 0.65, 0.95)  (0.32, 0.63, 1.22) 
Table 14.
Aggregated normalization of the fuzzy decision matrix.
Table 14.
Aggregated normalization of the fuzzy decision matrix.
 C4  C8  C3  C1  C9  C5  C6 

A1  (0.14, 0.40, 0.69)  (0.22, 0.49, 0.86)  (0.55, 0.86, 1.29)  (0.16, 0.45, 0.77)  (0.21, 0.46, 0.95)  (0.12, 0.38, 0.71)  (0.06, 0.32, 0.70) 
A2  (0.53, 0.77, 0.96)  (0.50, 0.79, 1.13)  (0, 0.07, 0.32)  (0.24, 0.54, 0.82)  (0.26, 0.53, 1.04)  (0.42, 0.70, 0.98)  (0.43, 0.73, 1.14) 
A3  (0.37, 0.65, 0.92)  (0.08, 0.33, 0.68)  (0.16, 0.41, 0.78)  (0.51, 0.77, 0.96)  (0.41, 0.71, 1.26)  (0.50, 0.74, 0.98)  (0.35, 0.65, 1.09) 
Table 15.
The weighted fuzzy decision matrix.
Table 15.
The weighted fuzzy decision matrix.
 C4  C8  C3  C1  C9  C5  C6 

A1  (0.02, 0.07, 0.12)  (0.03, 0.08, 0.14)  (0.08, 0.13, 0.20)  (0.02, 0.06, 0.10)  (0.01, 0.05, 0.10)  (0.02, 0.05, 0.09)  (0.01, 0.04, 0.09) 
A2  (0.09, 0.13, 0.16)  (0.08, 0.12, 0.18)  (0, 0.01, 0.05)  (0.03, 0.07, 0.11)  (0.02, 0.06, 0.12)  (0.06, 0.09, 0.13)  (0.05, 0.09, 0.14) 
A3  (0.06, 0.11, 0.15)  (0.01, 0.05, 0.11)  (0.02, 0.06, 0.12)  (0.07, 0.10, 0.13)  (0.06, 0.10, 0.15)  (0.06, 0.10, 0.13)  (0.04, 0.08, 0.13) 
Table 16.
Summation of weighted fuzzy decision matrix per the criteria type.
Table 16.
Summation of weighted fuzzy decision matrix per the criteria type.
 ${{\stackrel{~}{\mathit{L}}}_{\mathit{i}}}^{^}$  ${{\stackrel{~}{\mathit{A}}}_{\mathit{i}}}^{^}$ 

A1  (0.52, 0.61, 0.71)  (0.20, 0.42, 0.64) 
A2  (0.35, 0.48, 0.60)  (0.43, 0.62, 0.79) 
A3  (0.49, 0.59, 0.69)  (0.38, 0.56, 0.74) 
Table 17.
Final ranking.
 ${\mathit{R}}_{\mathit{i}}$ 

A1  0.82 
A2  1.15 
A3  0.97 
 Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. 
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).