A New Hybrid MCDM Model : Sustainable Supplier Selection in a Construction Company

Sustainable development is one of the most important preconditions for preserving resources and balanced functioning of a complete supply chain in different areas. Taking into account the complexity of sustainable development and a supply chain, different decisions have to be made day-to-day, requiring the consideration of different parameters. One of the most important decisions in a sustainable supply chain is the selection of a sustainable supplier and, often the applied methodology is multi-criteria decision-making (MCDM). In this paper, a new hybrid MCDM model for evaluating and selecting suppliers in a sustainable supply chain for a construction company has been developed. The evaluation and selection of suppliers have been carried out on the basis of 21 criteria that belong to all aspects of sustainability. The determination of the weight values of criteria has been performed applying the full consistency method (FUCOM), while a new rough complex proportional assessment (COPRAS) method has been developed to evaluate the alternatives. The rough Dombi aggregator has been used for averaging in group decision-making while evaluating the significance of criteria and assessing the alternatives. The obtained results have been checked and confirmed using a sensitivity analysis that implies a four-phase procedure. In the first phase, the change of criteria weight was performed, while, in the second phase, rough additive ratio assessment (ARAS), rough weighted aggregated sum product assessment (WASPAS), rough simple additive weighting (SAW), and rough multi-attributive border approximation area comparison (MABAC) have been applied. The third phase involves changing the parameter ρ in the modeling of rough Dombi aggregator, and the fourth phase includes the calculation of Spearman’s correlation coefficient (SCC) that shows a high correlation of ranks.


Introduction
Sustainable engineering implies the execution of all processes and activities respecting all aspects of sustainability: economic, social, and environmental aspects.In addition, it is necessary to take into account the interactions and symmetry between them.This is confirmed by Hutchins et al. [1] process (LFPPFAHP) and extension theory.Salabun et al. [26] developed an MCDM model with COMET method for offshore wind farm localization.This method is also used in [27] for sustainable manufacturing and for solving the problem of the sustainable ammonium nitrate transport in [28].

Review of MCDM Methods for Sustainable Supplier Selection
The selection of suppliers is a constant process that requires the consideration of a certain number of criteria needed to make a decision on the selection of the most suitable suppliers [29][30][31].According to Yazdani et al. [32], supplier evaluation and selection is a significant strategic decision for reducing operating costs and improving organizational competitiveness to develop business opportunities.Therefore, it is necessary to pay special attention to the selection of suppliers, including all aspects of sustainability.
The supplier selection, according to many authors, is one of the most demanding problems of sustainable supply chain management [33].Fuzzy approach in combination with TOPSIS method is applied in [34] for assessing the sustainable performance of suppliers.In order to select suppliers in terms of sustainability, Dai and Blackhurst [35] present an integrated approach based on AHP and the quality function deployment (QFD) method.For the sustainable supplier selection, Azadnia et al. [36] propose an integrated approach that, in addition to the Fuzzy AHP method, is based on multi-objective mathematical programming, as well as on rule-based weighted fuzzy method.In [37], the assessment of sustainable supply chain management and the selection of suppliers are performed using grey theory in combination with the DEMATEL method, while Luthra et al. [38] present an integrated approach consisting of a combination of AHP and VIKOR method based on 22 criteria for all three aspects of sustainability.Sustainable supplier selection of raw materials in order to achieve sustainable development of the company is performed in [39], based on the fuzzy entropy-TOPSIS method.Hsu et al. [40] present a hybrid approach based on several MCDM methods in order to select suppliers in terms of carbon emissions.The evaluation of the supplier performance in the field of electronic industry in order to implement green supply chains is a topic of research in [41].The authors use rough DEMATEL-ANP (R'AMATEL) in combination with rough multi-attribute ideal real comparative analysis (R'MAIRCA) method.Liu et al. [42] select the suppliers of fresh products using best worst method (BWM) and multi-objective optimization on the basis of the ratio analysis (MULTIMOORA) method.Kusi-Sarpong et al. [43] present a framework for ranking and selecting the criteria for sustainable innovations in supply chain management based on the BWM method.A quantitative assessment of the performance of a sustainable supply chain is presented in [44] based on fuzzy entropy and fuzzy Multi-Attribute Utility Theory (MAUT) methods.Das and Shaw [45] propose a model based on AHP and fuzzy TOPSIS method for selecting a sustainable supply chain, taking into account carbon emissions and various social factors.Luthra et al. [46] propose the application of Delphi and fuzzy DEMATEL methods for identifying and evaluating guidelines for the application information and communication technologies in sustainable initiatives in supply chains.In [47], a framework that identifies sustainable processes in supply chains for individual industries in India is presented.The ranking of industry branches is carried out using six fuzzy MCDM methods.Liou et al. [48] are proposed hybrid model consists of DEMATEL, ANP, and COPRAS-G methods for improving green supply chain management.They have used 12 criteria for supplier selection in the electronic industry, and provided a systemic analytical model for the improvement of parts of the supply chain management.

Methods
Figure 1 presents the methodology used in this paper, which consists of four phases:

•
I-initial research and data collection  IV-sensitivity analysis method, and the creation of a hybrid MCDM model in the last step of this phase.
The third phase provides a detailed calculation for the evaluation and selection of a sustainable supplier, which consists of four steps.First, the determination of the criteria values using the FUCOM method is carried out, and then the transformation of the obtained values into rough numbers, in order to perform averaging using the rough Dombi aggregator and obtain the final values of the criteria.Subsequently, in the third step, the rough Dombi aggregator is again used to obtain an initial rough matrix, in order to make a decision on the selection of a sustainable supplier using the rough COPRAS method in the fourth step.The final phase is the sensitivity analysis already explained in the previous section.We have decided to extent the COPRAS method with rough numbers from following reasons.Rough set theory is vague, subjective, and imprecise, while the COPRAS method, according to Mulliner et al. [49], allows for both benefit and cost criteria to be incorporated with one analysis without difficulty or question.The main advantage of COPRAS method compared with other MCDM methods is to be able to show utility degree.Also, COPRAS method has a simple procedure to use.
The following is a brief summary of the FUCOM method algorithm in the first part and the detailed algorithm description of rough COPRAS method in the second part.

Full Consistency Method (FUCOM)
The FUCOM method has been developed by Pamučar et al. [50] for determining the weights of criteria.It is a new method that, according to authors, represents a better method than AHP (analytical hierarchy process) and BWM (best worst method).So far, it has been applied in studies [51][52][53][54].It consists of the following three steps: The first phase consists of five steps.First, recognition of the need for this research and definition of the problems and aims of the research are performed in the first two steps, and the MCDM model is formed in the third step.After forming the model and defining all elements, the criteria, the alternatives, and the team of experts, the processes of data collection begins, which is the fourth step of the first phase.In the last step, the evaluation of the mutual significance of the criteria and evaluation of the alternatives by the formed team of experts are carried out.The second phase consists of three steps, where the first one is collected data sorting and preparation for their insertion into the developed model.The second step is the development and detailed description of the rough COPRAS method, and the creation of a hybrid MCDM model in the last step of this phase.
The third phase provides a detailed calculation for the evaluation and selection of a sustainable supplier, which consists of four steps.First, the determination of the criteria values using the FUCOM method is carried out, and then the transformation of the obtained values into rough numbers, in order to perform averaging using the rough Dombi aggregator and obtain the final values of the criteria.Subsequently, in the third step, the rough Dombi aggregator is again used to obtain an initial rough matrix, in order to make a decision on the selection of a sustainable supplier using the rough COPRAS method in the fourth step.The final phase is the sensitivity analysis already explained in the previous section.
We have decided to extent the COPRAS method with rough numbers from following reasons.Rough set theory is vague, subjective, and imprecise, while the COPRAS method, according to Mulliner et al. [49], allows for both benefit and cost criteria to be incorporated with one analysis without difficulty or question.The main advantage of COPRAS method compared with other MCDM methods is to be able to show utility degree.Also, COPRAS method has a simple procedure to use.The following is a brief summary of the FUCOM method algorithm in the first part and the detailed algorithm description of rough COPRAS method in the second part.

Full Consistency Method (FUCOM)
The FUCOM method has been developed by Pamučar et al. [50] for determining the weights of criteria.It is a new method that, according to authors, represents a better method than AHP (analytical hierarchy process) and BWM (best worst method).So far, it has been applied in studies [51][52][53][54].It consists of the following three steps: Step 1.In the first step, the criteria from the predefined set of evaluation criteria C = {C 1 , C 2 , . . . ,C n } are ranked.The ranking is performed according to the significance of the criteria, i.e., starting from the criterion which is expected to have the highest weight coefficient to the criterion of the least significance.
Step 2. In the second step, a comparison of the ranked criteria is carried out and the comparative priority (ϕ k/(k+1) , k = 1, 2, . . ., n, where k represents the rank of the criteria) of the evaluation criteria is determined.
Step 3. In the third step, the final values of the weight coefficients of the evaluation criteria (w 1 , w 2 , . . . ,w n ) T are calculated.The final values of the weight coefficients should satisfy the two conditions: (1) that the ratio of the weight coefficients is equal to the comparative priority among the observed criteria (ϕ k/(k+1) ) defined in Step 2, i.e., that the following condition is met: (2) In addition to Condition (3), the final values of the weight coefficients should satisfy the condition of mathematical transitivity, i.e., that ϕ k/(k+1) ⊗ ϕ (k+1)/(k+2) = ϕ k/(k+2) .
Since ϕ k/(k+1) = w k w k+1 and ϕ (k+1)/(k+2) = is obtained.Thus, another condition that the final values of the weight coefficients of the evaluation criteria need to meet is obtained, namely Based on the defined settings, the final model for determining the final values of the weight coefficients of the evaluation criteria can be defined.minχ s.t.

A Novel Rough COPRAS Method
The COPRAS method was expanded with rough numbers as part of a sensitivity analysis in the research [55].So far, a complete algorithm that can enrich the theoretical field of multi-criteria decision-making has not been demonstrated.From this aspect, the algorithm presented below represents a significant contribution to the literature that addresses the problems of multi-criteria decision-making.It should be pointed out that the COPRAS method with interval rough numbers has been developed in [56], which differs from the proposed algorithm in this paper.
Rough COPRAS consists of the following steps: Step 1: Forming a multi-criteria model.In the initial step, it is necessary to create a multi-criteria model with all necessary elements.Create a set of n alternatives that will be evaluated based on m criteria assessed by e experts.
Step 2: Forming an initial matrix for group decision-making (6).In this step, it is necessary to transform the individual matrices formed by experts' evaluations into an initial group rough matrix.In order to achieve this, it is necessary to apply basic operations with rough numbers.
where RN(x ij ) is an estimated value of the ith alternative in relation to the jth criterion, n is the number of alternatives, and m is the number of criteria.
Step 3: Normalization of the initial rough decision-making matrix applying the linear normalization procedure (7).
Step 4: Forming a weighted normalized matrix using the following Formula (8): where r L ij ; r U ij is the normalized rough value of the ith alternative in relation to the jth criterion and w j is the weight or significance of the jth criterion.
Step 5: In this step, it is necessary to calculate the sum of the weighted normalized values for both types of criteria, for benefit criteria using Equation ( 9): and for cost criteria using Equation (10): Step 6: Determining the inverse summarized matrix for cost criteria (11): Symmetry 2019, 11, 353 8 of 24 Step 7: Determining the sum of the matrix for cost criteria (12) and the sum of its inverse matrix (13) so that two matrices 1 × 1 are obtained: Step 8: Determining the relative significance for each alternative.The relative weight Q i for the ith alternative is calculated applying Equation ( 14): Step 9: Determining the priorities of alternatives.The priority in comparing the alternatives is identified on the basis of their relative weight, where the alternative with a higher relative weight value is given a higher priority or a rank, and the alternative with a maximum value represents the most acceptable alternative.

Case Study
Sustainable supplier selection in the construction company was carried out on the basis of 21 criteria shown and explained in Table 1: economic, social, and environmental criteria.Each of these main criteria consists of seven subcriteria.The set of criteria used in this study was selected according to relevant literature, and based on interviews with authorized and managerial persons in the construction company.The first subcriterion that belongs to economic criteria C 11 (costs/prices) and the sixth subcriterion (consumption of resources) are the cost criteria, while the others are the benefit criteria.In this study, the team of five experts took part in the process of determination of weight coefficients of criteria and assessment of alternatives.Experts with a minimum of six years' experience in civil engineering were chosen.After interviewing the experts, the collected data were processed, and the aggregation of expert opinion was obtained.The collecting of data was carried out in the period from November 2018 until January 2019.

Determining Criteria Weights Using the FUCOM Method
In the following section, a detailed overview is provided of determining weight coefficients of the first-level criteria.
Step 1.In the first step, the decision-makers (DMs) ranked the criteria: DM Step 2. In the second step, the decision-makers compared, in pairs, the ranked criteria from step 1.The comparison is made according to the first-ranked criterion, based on the above scale [1, 7].This is how the importance of the criteria is obtained ( C j(k) ) for all the criteria ranked in step 1 (Table 2).
Based on the obtained significance of criteria, comparative significance values of criteria for each expert are calculated as follows: Step 3. Final values of weight coefficient should satisfy two conditions: (1) Final values of weight coefficient should satisfy the condition where (2) In addition to the defined relations, final values of weight coefficients should satisfy also the condition of mathematical transitivity, By applying Expression (5), the models for determining weight coefficients of the first-level criteria for each decision-maker can be defined: By solving the models presented, the values of weight coefficients for the first-level criteria for every decision-maker are obtained, as shown in Table 3.After the transformation has been completed, five rough matrices, to which the operations of the rough Dombi aggregator is applied, are obtained.As mentioned in the previous part of the paper, the research has involved five experts who are assigned the same weight values of 0.200.Based on the presented values, Expression (8) from [56], and assuming that ρ = 1 is at the position of C 1 , the aggregation of values is performed as follows: Similarly, the decision-makers have ranked the criteria of the second level and the significance of criteria is obtained (Table 4).Based on the calculation, in the same way as with the criteria on the first level of decision-making, the calculation for the second decision-making level is made, and the values are shown in Table 5 for a group of economic criteria, in Tables 6 and 7 for a group of social criteria, and in Tables 8 and 9 for a group of environmental criteria.Based on the significance of the groups of criteria (economic, social, and environmental) and applying Equation ( 5), the models for each decision-maker are formed.Solving these models, we obtain the values of weight coefficients per decision-makers (Table 10).The final values of weight coefficients by all criteria are obtained by multiplying the weight coefficients of the main criteria with the subcriteria of the group to which they belong.As can be seen from Table 10, the most important criteria belong to the group of economic and then environmental criteria, which is understandable with regard to the area of existence of the company in which the research has been carried out.

Ranking Alternatives Using a New Rough COPRAS Method
Table 11 presents the evaluation of alternatives according to all criteria based on the linguistic scale 1-7.In evaluating the alternatives, five decision-makers participated, whose expertise has already been described in the previous section.In order to be able to apply the developed methodology, the transformation of individual matrices into a group rough matrix is performed first.An example of calculating the value of the third alternative according to criterion C 11 is given below:

Table 11. Comparison of alternatives by five decision-makers.
Subsequently, the rough Dombi aggregator is applied and the final rough values of alternatives are obtained.The aggregation on the same example of the third alternative for criterion C 11 is carried out as follows: j=1 Lim(ϕ j ) In the same way, other values for all alternatives are obtained according to all the criteria, which creates the initial aggregated matrix shown in Table 12.The summarized values for each criterion, which are necessary for the application of normalization in the next step, are shown in the last row of Table 12.Applying Equation (7), the normalized value for the third alternative according to criterion C 11 will be The last row of Table 13 presents the values of the criteria obtained by applying the FUCOM method, which are necessary to create a weighted normalized matrix.The fourth step is the weighting of normalized rough matrix (Table 14) by multiplying all the values of the normalized matrix with the weights of the criteria by applying Equation (8).The next step is to summarize the values of the alternatives depending on the type of criteria, and two matrices are obtained.The first matrix refers to the sum of the values of alternatives according to the benefit group of criteria, while another one refers to cost criteria.In this research, cost criteria are C 11 and C 36 .
The matrix for alternatives according to benefit criteria is The matrix for alternatives according to cost criteria is 0.014, 0.037 0.015, 0.044 0.011, 0.037 0.020, 0.052 0.018, 0.053 An example of calculation is as follows: After that, it is necessary to calculate the inverse values of the matrix S − i by applying Equation ( 11), which is In the next step, it is first necessary to calculate the sum by column for cost criteria applying Equation (12), and the following values are obtained: and then applying Equation (13) to calculate the sum for the inverse matrix, which will be In the next step, it is necessary to determine the relative significance for each alternative.The relative weight Q i by alternatives is The ith alternative is calculated using Equation ( 14).An example of the calculation for the third alternative is In the last step, the alternatives are ranked from the highest to the lowest value, and the results are as follows:

Sensitivity Analysis and Discussion
The sensitivity analysis has been performed throughout four phases, the first of which involves the creation of nine scenarios where the weights of criteria are modeled.The second phase involves the application of different methods, that is, comparative analysis, while the third phase implies the change of the parameter ρ into the values of 1-10.The fourth phase includes the application of Spearman's correlation coefficient for the ranks of alternatives throughout the first two phases.
Figure 2 presents the ranks of alternatives throughout nine scenarios.The first scenario implies that all criteria are equally important, while in the second one, the six most important criteria (C 11 , C 12 , C 13 , C 16 , C 22 , C 33 ) are reduced by 4%, and others are increased by 2%.In the third set, the six most important criteria are eliminated, and in the fourth one, the most important criteria are increased by 4%, while the rest are reduced by 2%.The fifth scenario involves the elimination of seven least important criteria (C 23 , C 24 , C 25 , C 27 , C 34 , C 36 , and C 37 ).In the sixth set, the criteria that belong to the economic group are reduced by 4%, while the criteria of the social group are proportionally increased.The values of environmental criteria remain unchanged.The seventh set implies a reverse situation from the aspect of economic and social criteria in relation to the sixth set.In the eighth scenario, decision-making is based only on economic criteria, and in the ninth scenario, only on environmental criteria.that all criteria are equally important, while in the second one, the six most important criteria (C11, C12, C13, C16, C22, C33) are reduced by 4%, and others are increased by 2%.In the third set, the six most important criteria are eliminated, and in the fourth one, the most important criteria are increased by 4%, while the rest are reduced by 2%.The fifth scenario involves the elimination of seven least important criteria (C23, C24, C25, C27, C34, C36, and C37).In the sixth set, the criteria that belong to the economic group are reduced by 4%, while the criteria of the social group are proportionally increased.The values of environmental criteria remain unchanged.The seventh set implies a reverse situation from the aspect of economic and social criteria in relation to the sixth set.In the eighth scenario, decision-making is based only on economic criteria, and in the ninth scenario, only on environmental criteria.The ranks of alternatives do not change in the fourth, fifth, sixth, and eighth criteria, which implies that the most important criteria play a very important role in the decision-making process in this research.This is confirmed by the fact that there are significant changes in the rankings in the first and third sets when all the criteria are equal, i.e., when the six most important ones are eliminated.In other scenarios there are no significant changes.It is important to emphasize that the two alternatives that represent the best solution, A3 and A2, do not change ranks in any scenario, which implies that they are insensitive to the changes in the significance of the criteria.
Figure 3 shows the comparison of the proposed model with other approaches developed recently: rough WASPAS [57], rough MABAC [58], rough SAW [59], and rough ARAS [60].The ranks of alternatives do not change in the fourth, fifth, sixth, and eighth criteria, which implies that the most important criteria play a very important role in the decision-making process in this research.This is confirmed by the fact that there are significant changes in the rankings in the first and third sets when all the criteria are equal, i.e., when the six most important ones are eliminated.In other scenarios there are no significant changes.It is important to emphasize that the two alternatives that represent the best solution, A 3 and A 2 , do not change ranks in any scenario, which implies that they are insensitive to the changes in the significance of the criteria.
Figure 3 shows the comparison of the proposed model with other approaches developed recently: rough WASPAS [57], rough MABAC [58], rough SAW [59], and rough ARAS [60].Observing the results obtained by other methods, the stability of the two best alternatives do not come into question, since they continue to take the first two positions using all the methods.The highest similarities of ranks obtained with rough COPRAS have the alternatives obtained with rough ARAS, where only the first and fifth alternatives change their positions.Slightly bigger changes in ranks are found with other methods.
Table 14 presents the part of the sensitivity analysis that relates to the change of parameter ρ.
Table 14.Ranks of alternatives depending on the change of parameter ρ.Observing the results obtained by other methods, the stability of the two best alternatives do not come into question, since they continue to take the first two positions using all the methods.The highest similarities of ranks obtained with rough COPRAS have the alternatives obtained with rough Table 15 presents the part of the sensitivity analysis that relates to the change of parameter ρ.
Table 15.Ranks of alternatives depending on the change of parameter ρ.

Parameter ρ
Changing the parameter ρ does not change significantly the initial results obtained.For the parameters ρ = 1-6, the same ranks are obtained as with the hybrid FUCOM-rough COPRAS model.The only changes in ranks are for parameters ρ = 7, ρ = 8, and ρ = 10 when the fourth and fifth alternative belongs to the same rank, and when ρ = 9, the fourth and fifth alternative change their positions while others remain unchanged.Based on the overall sensitivity analysis with the change of parameter ρ, it can be concluded that the model is not sensitive to these changes.
At the end of the sensitivity analysis, the calculated Spearman's correlation coefficient for the first two phases is given (Table 16).For the third phase, calculation is not performed, since it is obvious that there is almost a complete correlation and, as already mentioned, the change of this parameter does not significantly affect the ranking of the alternatives.Concerning the first phase of the sensitivity analysis in which the weights of the criteria change in sets, it can be seen that the model is sensitive to their changes.The initial set has a full correlation with four sets (4, 5, 6, and 8), while the smallest correlation SCC = 0.600 is with the first and third set, in which the ranks of two alternatives change for a total of three positions.In the second and seventh sets, the two last alternatives change positions between each other, so SCC = 0.900 with the initial set.In the ninth set, there is a change in the rank of three alternatives with SCC = 0.700.The total average value of SCC is 0.856, which represents a high correlation of ranks, regardless of the changes mentioned.
In the second phase, it can be observed that rough COPRAS has the highest correlation with the rough ARAS method of 0.900, while with other methods, rough WASPAS, rough SAW, and rough MABAC, SCC = 0.700.Taking this into account, it is concluded that rough WASPAS, rough SAW, and rough MABAC have a complete correlation, which ultimately implies that the average SCC = 0.920, which is a very high correlation of ranks.

Conclusions
This paper has proposed a new hybrid model that integrates FUCOM with the rough COPRAS method using the rough Dombi aggregator.This is the first time in the literature that this kind of model has been applied, that integrates the positive aspects of FUCOM method, rough set theory, rough Dombi aggregator for group decision-making, and the COPRAS method, which is one of the main contributions of this paper.In addition, the detailed and demonstrated algorithm of the rough COPRAS method also contributes to the overall field of multi-criteria decision-making.
Based on the 21 criteria of sustainability, a total of five suppliers in a construction company were considered, where it was concluded that the third and second suppliers are the best solutions regardless of any change in the model.This has been proven throughout a comprehensive sensitivity analysis in which different scenarios-with a change in the weight of criteria-were formed.The two mentioned alternatives are not sensitive to any changes in the values of the criteria.In addition, neither the change of parameter ρ, which is an integral part of the rough Dombi aggregator, affects the rankings of the third and second supplier, which has been confirmed by comparison with other approaches.The best solution in this model is completely insensitive, i.e., stable, while the ranks of other alternatives vary depending on the method of modeling the sensitivity analysis.
The developed model can be useful in other areas of engineering, but also when making real life decisions, since it adequately treats uncertainties by applying the theory of rough sets and subjectivity by applying the FUCOM method.Thus, it is possible to make more accurate and valid decisions that can have a huge impact on a sustainable supply chain.Future research related to this study will address the development and application of a similar model with the FUCOM method and an uncertainty theory, e.g., grey theory.

Figure 1 .
Figure 1.Research flow with proposed model.

Figure 1 .
Figure 1.Research flow with proposed model.

Figure 2 .
Figure 2. Sensitivity analysis by changing the weight values of criteria.

Figure 2 .
Figure 2. Sensitivity analysis by changing the weight values of criteria.

Symmetry 2019 , 24 Figure 3 .
Figure 3.Comparison of the results of the developed model with other methods.

Figure 3 .
Figure 3.Comparison of the results of the developed model with other methods.
only the first and fifth alternatives change their positions.Slightly bigger changes in ranks are found with other methods.

Table 1 .
Criteria for sustainable supplier selection.
C 17 Partnership relationsDetermining the willingness to establish long-term and close business relations with suppliers to jointly develop the market

Table 2 .
Significance of criteria.

Table 3 .
Values of weight coefficients for the first level of decision-making according to each DM.The final values shown in the last column of Table3are obtained by rough operations and the rough Dombi aggregator.First, the transformation of individual matrices into a group rough matrix is performed as follows:c 1 = {0.552, 0.574, 0.618, 0.282, 0.465}, = [0.282, 0.498]; RN c 5 1 = [0.373, 0.552].Subsequently, the rough Dombi aggregator is applied and final rough values of the criteria at the first decision-making level are obtained.The aggregation is performed as follows.

Table 4 .
The ranking and significance of the second-level criteria for a group of economic factors.

Table 5 .
Values of weight coefficients for the second decision-making level according to each decision-maker for a group of economic criteria.

Table 6 .
The ranking and significance of the second-level criteria for a group of social factors.

Table 7 .
Values of weight coefficients for the second decision-making level according to each decision-maker for a group of social criteria.

Table 8 .
The ranking and significance of the second-level criteria for a group of environmental factors.

Table 9 .
Values of weight coefficients for the second decision-making level according to each decision-maker for a group of environmental criteria.

Table 10 .
Final values of criteria.

Table 16 .
Spearman's correlation coefficient for the first two phases of sensitivity analysis.