Application of PROMETHEE Method in Evaluation ... Radojko Lukic, PhD Application of PROMETHEE Method in Evaluation of Insurance Efficiency in Serbia DOI: https://doi.org/10.55707/eb.v10i1.121 Izvirni znanstveni članek UDK 005.336.1:368(497.11) KLJUČNE BESEDE: učinkovitost, zavarovanje, PROMETHEE metoda POVZETEK – Vprašanje ocenjevanja učinkovitosti zavarovanja na podlagi metod večkriterijske analize je zelo aktualno, kompleksno in pomembno. Zagota- vlja osnovo za izboljšanje učinkovitosti zavarovanja z ustreznimi ukrepi v prihodnosti. S tem v mislih je v prispevku analizirana učinkovitost zavarovanja v Srbiji po metodi PROMETHEE. Dobljeni rezultati empirične raziskave zavarovalniške učinkovitosti v Srbiji po metodi PROMETHEE kažejo, da je bilo to najboljše v letu 2020. V zadnjem času se učinkovi- tost zavarovalništva v Srbiji nenehno povečuje. Na to so pozitivno vplivali številni dejavniki: gospodar- ska klima, življenjski standard, zaposlenost, sodobni koncepti upravljanja stroškov, prihodkov in dobič- ka, elektronska prodaja zavarovalnih produktov in digitalizacija celotnega poslovanja. Negativni vpliv pandemije covida-19 na učinkovitost zavarovanja v Srbiji je zanemarljiv (v primerjavi z drugimi proizvo- dnimi dejavnostmi, kot sta turizem in gostinstvo) in se delno izravnava s povečano prodajo zavarovalnih produktov na spletu in infrastrukturnim (premoženj- skim) zavarovanjem. Original scientific article UDC 005.336.1:368(497.11) KEYWORDS: efficiency, insurance, PROMETHEE method ABSTRACT – The issue of evaluating the efficiency of insurance based on the methods of multi-criteria analysis is very current, complex and significant. It provides a basis for improving the efficiency of in- surance by applying adequate measures in the future. With this in mind, the paper analyzes the efficiency of insurance in Serbia using the PROMETHEE method. The results obtained from the empirical research of insurance efficiency in Serbia using the PRO- METHEE method show that it was most efficient in 2020. Recently, the efficiency of insurance in Serbia has been increasing continuously. This has been posi- tively influenced by numerous factors: economic cli- mate; living standard; employment; modern concepts of cost, income and profit management; electronic sa- les of insurance products; digitalization of the entire business. The negative impact of the COVID-19 pan- demic on insurance efficiency in Serbia is negligible (compared to other production activities, such as tou- rism and hospitality) and is partly offset by increased sales of insurance products online and infrastructure (property) insurance. There is a growing understan- ding of the importance of insuring against potential risks of all kinds. 1 Introduction The importance of evaluating the efficiency of insurance based on the methods of multi-criteria analysis is growing (Beiragh, 2020). Starting from that, the subject of this research paper is the analysis of insurance efficiency in Serbia based on the PROMETHEE method. The goal and purpose of this is to address this issue as com- plexly as possible using qualitative and especially quantitative methods in order to gain knowledge about the real efficiency of insurance companies in Serbia, as a star- Prejeto/Recived: 10. 1. 2023 Sprejeto/Accepted: 17. 3. 2023 Besedilo/Text © 2023 Avtor(ji)/The Author(s) To delo je objavljeno pod licenco CC BY Priznanje avtorstva 4.0 Mednarodna. / This work is published under a CC BY Attribution 4.0 International license. https://creativecommons.org/licenses/by/4.0/ 4 Revija za ekonomske in poslovne vede (1, 2023) ting point for future improvement by taking appropriate measures. This, among other things, reflects the scientific and professional contribution of this paper. Lately, there is increasingly rich literature dedicated to evaluating the efficiency of all companies, which includes insurance companies, based on multi-criteria analysis. In this context, the role and importance of the PROMETHEE method is growing. In the relevant literature, there is, as far as we know, no comprehensive work dedicated to the evaluation of insurance efficiency in Serbia using the PROMETHEE method (Kočović, 2010; Lukić, 2016, 2018, 2021; Mandić, 2017; Rakonjac Antić, 2018). This gap should be somewhat filled by this paper, which, among other things, reflects its scientific and professional contribution. The basic research hypothesis in this paper is based on the fact that continuous analysis and control of critical factors is a prerequisite for improving the efficiency of insurance in Serbia in the future by taking appropriate measures and effectively controlling their implementation. The application of the PROMETHEE method also plays a significant role in this. The research methodology is based on the application of the AHP and PRO- METHEE methods. In order to make the quantitative analysis of the researched pro- blem as complex as possible, statistical analysis is used to some extent. For the purpose of researching the problem addressed by this paper using the gi- ven methodology, empirical data were collected from the Serbian Business Registers Agency. They have been generated in accordance with the relevant international stan- dards, so there are no restrictions on international comparison. 2 PROMETHEE method The PROMETHEE method is based on the comparison of paired alternatives accor- ding to each criterion. For each criterion, the decision maker considers a particular fun - ction of preference (Brans, 2010, 2016; Podvezko, 2010; Stanitsas, 2021). The prefe- rence can take a value in the range from 0 to 1. Different variants of the PROMETHEE method have been developed (I, II, III, IV , V , VI). There is also a visual interactive modulation of GAIA that represents a graphical interpretation of the PROMETHEE method. The PROMETHEE method is simple, allows partial and complex ranking of al- ternatives (PROMETHEE I and PROMETHEE II, respectively), and has wide practical application (in banking, investment, medicine, chemistry, trade, tourism, etc.). The PROMETHEE method takes place in several steps (Polat, 2015; Geldermann et al., 2000; Behzadian et al., 2010; Mohammadi, 2017; Abdullah, 2019; Brans et al., 1984, 1985, 1986, 1994, 2016). Those steps are: □ Step 1: Defining the criteria (j = 1, ..., k) and a set of possible alternatives in de- cision making. 5 Radojko Lukic, PhD: Application of PROMETHEE Method in Evaluation ... □ Step 2: Determining the weight wj of the criterion. It shows the relative importan- ce of each criterion, where the sum of the weights of the criterion equals one, i.e.: □ Step 3: Normalize the decision matrix in the interval from 0 to 1 using the fol- lowing equation: (i = 1.2 ..., n; j = 1.2 ..., m) where Xij is the estimated value by decision makers i = 1, ..., n, and the number of criteria j = 1, ..., m. □ Step 4: Determine the deviation of comparable pairs. where dj (a, b) denotes the differences between the evaluations a and b of each criterion. □ Step 5: Defining the preference functions P j (a,b)=F j [d j (a,b)], where P j (a, b) represents the function of the difference between the evaluation of alternative a in relation to alternative b for each criterion in the interval from 0 to 1. A smaller number of functions indicates the indifference of decision makers. Conver- sely, values closer to 1 indicate greater preference. □ Step 6: Define the multi-criteria preference index where wj indicates the weight of the criterion. The symbol π (a, b) shows the de- gree of preference of a in relation to b for all criteria. π (a, b) ≈ 0 implies a weak preference of a over b. π (a, b) ≈ 1 implies a strong preference of a over b. □ Step 7: Obtain the order of preferences. in this step, the ranking can be performed partially or completely. Partial ranking can be obtained using PROMETHEE I. In case a complete ranking is needed, it inclu- des an additional step by applying PROMETHEE II. Partial ranking of alternatives (PROMETHEE I): 6 Revija za ekonomske in poslovne vede (1, 2023) where represents a positive output flow (how many alternatives a domi- nate over other alternatives), and represents a negative input flow (how many alternatives are preferred by all the other alternatives). An alternative with a high value and a lower value is the best alternative. Preferential ratio and partial rankings are performed as follows: However, not all alternatives are comparable. It is therefore necessary to calculate the net flow in the next step. (b) Complex ranking of alternatives (PROMETHEE II). The complex ranking of alternatives can avoid incomparability. where denotes the net flow for each alternative. Relationship preferences are as follows: Thus, all alternatives are capable of being comparable based on the value . The highest value indicates the most desirable alternative. In the calculation procedure, most of the steps are fixed, except for step 5. In this step, the choice of the preference function is arbitrary depending on the characteristics of the criteria and the preference of the decision makers. Special attention is paid to the choice of the preference function because it can affect the final net value. 3 Preference functions The PROMETHEE method uses preference functions to define deviations betwe- en alternatives for each criterion. The PROMETHEE method uses six preference fun- ctions to express the significance of the alternative for each criterion/factor, as well as the difficulty to express the relative importance of each criterion. These functions are: 7 Radojko Lukic, PhD: Application of PROMETHEE Method in Evaluation ... Type I – The usual preference function is a basic type of function that does not contain any parameters and is used very rarely (Figure 1). Type II – The U-shape function contains only the indifference threshold (Figure 2) Parameter q. 8 Revija za ekonomske in poslovne vede (1, 2023) Type III – The V-shape function contains only the preference threshold (Figure 3). It differs from the previous one because the preference is defined as the proportional deviation of the alternatives in the value range from 0 to m. Parameter p. Type IV – The Level function contains the indifference threshold n and the prefe- rence threshold m. 9 Radojko Lukic, PhD: Application of PROMETHEE Method in Evaluation ... Type V – The Linear function contains the indifference threshold n and the pre- ference threshold m. It is proportional to the deviation of alternatives in the interval from - n - m to + n + m (Figure 5). Parameters p, q. Type VI – The Gaussian function contains only the Gaussian threshold σ and is used less frequently (Figure 6). 10 Revija za ekonomske in poslovne vede (1, 2023) 4 Method of analytic hierarchy process (AHP) Considering that the weighting coefficients (weights) of the criterion when applying the PROMETHEE method are determined using the AHP method, we will briefly look at its theoretical and methodological characteristics. The Analytic Hierarchy Process (AHP) method takes place through the following steps (Saaty, 2008): 5 Measuring insurance efficiency in Serbia based on AHP/ PROMETHEE methods: results and discussion When measuring the efficiency of insurance in Serbia on the basis of the PRO- METHEE method, the following criteria were used: C1 – number of employees, C2 – assets, C3 – capital, C4 – business (functional) income, C5 – net profit. Alternatives were observed in the following years: A1 – 2013, A2 – 2014, A3 – 2015, A4 – 2016, A5 – 2017, A6 – 2018, A7 – 2019 and A8 – 2020. 11 Radojko Lukic, PhD: Application of PROMETHEE Method in Evaluation ... Table 1 shows the initial data for measuring the efficiency of insurance in Serbia for the period 2013 – 2020. Table 1 Initial data for measuring the efficiency of insurance in Serbia Number of employees Assets The capital Operating (functional) income Net profit 2013 10918 138052 28617 55424 2009 2014 11295 167768 35177 58747 2900 2015 11252 191796 44795 70572 4625 2016 11043 215589 50816 79017 6009 2017 10894 232968 53981 82209 6634 2018 10649 279227 61703 86850 9072 2019 10917 299739 72147 92194 11680 2020 11164 314197 76871 95274 13003 Note. Data are expressed in millions of dinars. The number of employees is expressed in whole numbers. Source: Serbian Business Registers Agency Table 2 shows the statistics of the initial data. Table 2 Statistics Statistics Number of employees Assets The capital Operating (functional) income Net profit N Valid 8 8 8 8 8 Missing 0 0 0 0 0 Median 10980.5000 224278.5000 52398.5000 80613.0000 6321.5000 Std. Deviation 215.52129 63672.36835 16892.16762 14787.73070 3976.85730 Minimum 10649.00 138052.00 28617.00 55424.00 2009.00 Maximum 11295.00 314197.00 76871.00 95274.00 13003.00 NPar Tests Friedman test Ranks Mean Rank 1.75 5.00 3.00 4.00 1.25 Test Statistics a N 8 Chi-Square 308.00 df 4 Asymp. Sig. .000 a. Friedman Test Note. Author's calculation using the SPSS software program. The data in the table above show that the values of all observed variables from 2016 were above average. This had a positive effect on the efficiency of insurance in Serbia. Seeing that Asymp. Sig. = .000 < .05, the hypotheithat the differences between 12 Revija za ekonomske in poslovne vede (1, 2023) the variables (measurements) are equal to zero is rejected, i.e., the hypothesis that the differences between them are statistically significant is accepted. Table 3 shows the correlation matrix of the initial data. Table 3 Correlation matrix Correlations 1 2 3 4 5 1 Number of employees Pearson Correlation 1 -.319 -.245 -.336 -.255 Sig. (2-tailed) .441 .558 .416 .541 N 8 8 8 8 8 2 Assets Pearson Correlation -.319 1 .993 ** .976 ** .988 ** Sig. (2-tailed) .441 .000 .000 .000 N 8 8 8 8 8 3 Capital Pearson Correlation -.245 .993 ** 1 .982 ** .993 ** Sig. (2-tailed) .558 .000 .000 .000 N 8 8 8 8 8 4 Operating (functional) income Pearson Correlation -.336 .976 ** .982 ** 1 .960 ** Sig. (2-tailed) .416 .000 .000 .000 N 8 8 8 8 8 5 Net profit Pearson Correlation -.255 .988 ** .993 ** .960 ** 1 Sig. (2-tailed) .541 .000 .000 .000 N 8 8 8 8 8 **. Correlation is significant at the 0.01 level (2-tailed). Note. Author's calculation using the SPSS software program. The correlation matrix shows that there is a strong correlation between the analyzed variables at the level of statistical significance (Sig. (2-tailed) = .000 < .05), except for the number of employees. Improving the efficiency of insurance through a more efficient management of assets, capital, business (functional) revenues and profits can have a significant impact. In this regard, it is also necessary to significantly improve the efficiency of human resource management through training, career advancement, flexible employment and working hours, and an adequate remuneration system. The sale of insurance products via the Internet also plays a significant role in all this. The weighting coefficients (weights) of the criteria were determined using the AHP method (Saaty, 2008). They are shown in Table 4 and Figure 7. Table 4 Weighting coefficients of the criteria AHP With Arirthmetic Mean Method Initial Comparisons Matrix C1 C2 C3 C4 C5 C1 1 2 2 2 1 C2 0.5 1 1 1 2 C3 0.5 1 1 0.5 1 C4 0.5 1 2 1 1 C5 1 0.5 1 1 1 13 Radojko Lukic, PhD: Application of PROMETHEE Method in Evaluation ... SUM 3.5 5.5 7 5.5 6 Normalized Matrix C1 C2 C3 C4 C5 Weights of Criteria C1 0.2857 0.3636 0.2857 0.3636 0.1667 0.2931 C2 0.1429 0.1818 0.1429 0.1818 0.3333 0.1965 C3 0.1429 0.1818 0.1429 0.0909 0.1667 0.1450 C4 0.1429 0.1818 0.2857 0.1818 0.1667 0.1918 C5 0.2857 0.0909 0.1429 0.1818 0.1667 0.1736 SUM 1 Consistency Ratio 0.0483 COMPARE WITH 0.1; IT SHOULD BE LESS THAN 0.1. Note: Author's calculation using AHPSoftware-Excel software Figure 7 Ranking of criteria Source: Author's picture 14 Revija za ekonomske in poslovne vede (1, 2023) Ranked in first place is the criterion of the number of employees. It is followed by the criteria of assets, operating (functional) income, net profit and capital. This indica- tes that more efficient human capital management can, among other things, significan - tly influence the achievement of the target insurance efficiency in Serbia. Table 5 shows the initial matrix of the PROMETHEE method. Table 5 Initial matrix ⱷ 0.2 C1 C2 C3 C4 C5 Type max max max max Max Type P . Function 1st type 1st type 1st type 1st type 1st type Weight 0.2931 0.1965 0.1450 0.1918 0.1736 A 1 10918 138052 28617 55424 2009 A 2 11295 167768 35177 58747 2900 A 3 11252 191796 44795 70572 4625 A 4 11043 215589 50816 79017 6009 A 5 10894 232968 53981 82209 6634 A 6 10649 279227 61703 86850 9072 A 7 10917 299739 72147 92194 11680 A 8 11164 314197 76871 95274 13003 Note: Author's expression Table 6 shows the flows of the PROMETHEE method. Table 6 Flows Fluxes F + F- F 2013 A 1 0.1256 0.8744 -0.7488 2014 A 2 0.3941 0.6059 -0.2118 2015 A 3 0.4532 0.5468 -0.0936 2016 A 4 0.4704 0.5296 -0.0591 2017 A 5 0.4458 0.5542 -0.1084 2018 A 6 0.5049 0.4951 0.0099 2019 A 7 0.6897 0.3103 0.3793 2020 A 8 0.9163 0.0837 0.8325 Note: Author's expression Table 7 shows the summary result of the PROMETHEE method - ranking of al- ternatives. 15 Radojko Lukic, PhD: Application of PROMETHEE Method in Evaluation ... Table 7 Result - Ranking of alternatives Method Promethee I Promethee I Promethee II Promethee II Promethee III Promethee III Direction 1 A 8 A 8 A 8 A 8 A 8 A 8 2 A 7 A 7 A 7 A 7 A 7 A 7 3 A 6 A 6 A 6 A 6 A2 A3A 4 A 5 A 6 A2 A3A 4 A 5 A 6 4 A 4 A 4 A 4 A 4 A 1 A 1 5 A 3 A 3 A 3 A 3 6 A 5 A 5 A 5 A 5 7 A 2 A 2 A 2 A 2 8 A 1 A 1 A 1 A 1 Note: Author's presentation of results Figure 8 shows the flows of the PROMETHEE method. Figure 8 Flows Source: Author's picture The results obtained from the empirical research of insurance efficiency in Serbia in the period 2013 – 2020 using the PROMETHEE method show that it has been continuously increasing in recent years. It was the most efficient in 2020. Such a trend of insurance efficiency in Serbia was influenced by numerous macro and micro fa - ctors, such as: economic climate; employment; interest rate; exchange rate; inflation; a growing understanding of the importance of insurance against potential risks of all kinds; digitalization of the entire business, etc. The impact of the COVID-19 pande- 16 Revija za ekonomske in poslovne vede (1, 2023) mic on insurance efficiency in Serbia is negligible. It is largely neutralized by selling insurance products electronically. 6 Conclusion Based on the results obtained from the empirical research of insurance efficien- cy in Serbia in the period 2013 – 2020 using the PROMETHEE method, it can be concluded that it has been continuously increasing in recent years. It was the most efficient in 2020. Such a trend of insurance efficiency in Serbia was influenced by numerous macro and micro factors, such as: economic climate; employment; interest rate; exchange rate; inflation; a growing understanding of the importance of insurance against potential risks of all kinds; digitalization of the entire business, etc. The impact of the COVID-19 pandemic on insurance efficiency in Serbia is negligible. It is largely neutralized by selling insurance products electronically. In order to increase the efficiency of insurance in Serbia in the future, it is ne- cessary to manage human resources, assets, capital, sales of insurance products, and profits as efficiently as possible. The digitalization of the entire business also plays an important role in this. The application of the PROMETHEE method in analyzing insurance efficiency in Serbia provides more reliable results in relation to the ratio analysis as a basis for future improvements by taking appropriate measures and adequately controlling their implementation. Therefore, it should be used especially in combination with other methods of multi-criteria decision making (TOPSIS, ARAS, AHP, etc.). Dr. Radojko Lukić Uporaba metode PROMETHEE pri ocenjevanju učinkovitosti zavarovanja v Srbiji Pomen vrednotenja učinkovitosti zavarovanja na podlagi metod večkriterijske analize narašča (Beiragh, 2020). Izhajajoč iz tega je predmet raziskave v prispevku analiza učinkovitosti zavarovanja v Srbiji po metodi PROMETHEE. Cilj in namen tega je čim bolj kompleksno obravnavati to problematiko kvalitativno in predvsem kvantitativno, da bi pridobili znanje o resnični učinkovitosti zavarovalnic v Srbiji kot izhodišču za prihodnje izboljšave z ustreznimi ukrepi. To med drugim odraža znanstve- ni in strokovni vidik tega prispevka. V zadnjem času je vse bogatejša literatura, posvečena ocenjevanju učinkovito- sti vseh podjetij, kar pomeni za zavarovalnice ocenjevanje na podlagi večkriterijske analize. V tem kontekstu naraščata vloga in pomen metode PROMETHEE. V rele- vantni literaturi, kolikor nam je znano, ni celovitega dela, posvečenega vrednotenju 17 Radojko Lukic, PhD: Application of PROMETHEE Method in Evaluation ... zavarovalne učinkovitosti v Srbiji po metodi PROMETHEE (Kočović, 2010; Lukić, 2016, 2018, 2021; Mandić, 2017; Rakonjac Antić, 2018). To vrzel bi moral prispevek nekoliko zapolniti, kar med drugim odraža njegov znanstveni in strokovni prispevek. Osnovna hipoteza raziskave obravnavane problematike v prispevku temelji na dejstvu, da je nenehna analiza in obvladovanje kritičnih dejavnikov pogoj za izbolj- šanje učinkovitosti zavarovanja v Srbiji v prihodnosti z ustreznimi ukrepi in nadzorom njihovega učinkovitega izvajanja. Pri tem ima pomembno vlogo tudi uporaba metode PROMETHEE. Raziskovalna metodologija te domneve temelji na uporabi metod AHP in PRO- METHEE. Da bi bila kvantitativna analiza obravnavane težave v prispevku čim bolj kompleksna, se do neke mere uporablja statistična analiza. Za namene raziskovanja problematike, obravnavane v prispevku, so bili z upora- bo podane metodologije zbrani empirični podatki Agencije za poslovne registre Repu- blike Srbije. Predstavljeni so v skladu z ustreznimi mednarodnimi standardi, tako da ni omejitev za mednarodno primerjavo. Pri merjenju učinkovitosti zavarovanja v Srbiji na podlagi metode PROMETHEE so bila uporabljena naslednja merila: C1 ‒ število zaposlenih, C2 ‒ sredstva, C3 ‒ kapital, C4 ‒ poslovni (funkcionalni) dohodek, C5 ‒ čisti dobiček. Alternative so bile opazovane v letih: A1 ‒ 2013, A2 ‒ 2014, A3 ‒ 2015, A4 ‒ 2016, A5 ‒ 2017, A6 ‒ 2018, A7 ‒ 2019 in A8 ‒ 2020. Statistika začetnih podatkov v podani tabeli kaže, da so bile vrednosti vseh opazo- vanih spremenljivk iz leta 2016 nadpovprečne. To je pozitivno vplivalo na učinkovitost zavarovanja v Srbiji. Tako je Asimp. Sig. = ,000 <,05. Hipotezo, da so razlike med spremenljivkami (meritvami) enake nič, zavrnemo, torej sprejmemo hipotezo, da so razlike med njimi statistično pomembne. Korelacijska matrika kaže, da obstaja močna korelacija med analiziranimi spre- menljivkami na ravni statistične pomembnosti (Sig. (2-tailed) = ,000 <,05), razen za število zaposlenih. Izboljšanje učinkovitosti zavarovanja z učinkovitejšim upravlja- njem premoženja, kapitala, poslovnih (funkcionalnih) prihodkov in dobičkov ima lahko pomemben vpliv. V zvezi s tem je treba tudi bistveno izboljšati učinkovitost upravljanja s človeškimi viri z izobraževanjem, kariernim napredovanjem, fleksibil- nim zaposlovanjem in delovnim časom ter ustreznim sistemom nagrajevanja. Pri vsem tem pomembno vlogo igra tudi prodaja zavarovalnih produktov preko spleta. Utežni koeficienti (uteži) kriterijev so bili določeni po metodi AHP (Saaty, 2008). Na prvem mestu je merilo število zaposlenih. Potem so tu še: sredstva, poslov- ni (funkcionalni) prihodki, čisti dobiček in kapital. To kaže, da lahko učinkovitejše upravljanje s človeškim kapitalom med drugim pomembno vpliva na doseganje ciljne zavarovalne učinkovitosti v Srbiji. Dobljeni rezultati empirične raziskave zavarovalniške učinkovitosti v Srbiji v ob- dobju 2013‒2020 po metodi PROMETHEE kažejo, da se ta v zadnjem času nenehno povečuje. V letu 2020 je bila najboljša. Na takšen trend zavarovalniške učinkovito- sti v Srbiji so vplivali številni makro in mikro dejavniki, kot so: gospodarska klima, 18 Revija za ekonomske in poslovne vede (1, 2023) zaposlenost, obrestna mera, devizni tečaj, inflacija, vse večje razumevanje pomena zavarovanja pred morebitnimi tveganji vseh vrst, digitalizacija celotnega poslovanja itd. Vpliv pandemije covida-19 na učinkovitost zavarovanja v Srbiji je zanemarljiv. V veliki meri se nevtralizira z elektronsko prodajo zavarovalnega produkta. LITERATURE 1. Abdullah, A, Chan, W. and Afshari, A. (2019). Application of PROMETHEE method for green supplier selection: a comparative result based on preference functions. Journal of Industrial Engineering International, (15), 271–285. https://doi.org/10.1007/s40092-018-0289-z 2. Behzadian, Majid, Reza Baradaran Kazemzadeh, Amir Albadvi, & Mohammad, Aghdasi (2010). Promethee: A Comprehensive Literature Review on Methodologies and Applications. 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