Green Supplier Selection Based on the Combination of Fuzzy SWARA (SWARA-F) and Fuzzy MARCOS (MARCOS-F) Methods

Year 2022, , 1535 - 1554, 01.12.2022

Ayşegül Tuş , Esra Aytaç Adalı

https://doi.org/10.35378/gujs.978997

Cited By: 8

Abstract

The green supply chain operations try to minimize environmental impact over the product's lifetime including product recycling or use, reduction of harmful substances, resource saving, green design, etc. Supplier selection is the vital issue in green purchasing. This paper aims to develop applicable and efficient methodology for green supplier selection. The proposed methodology includes the combination of Fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA-F) and Fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (MARCOS-F) methods. Fuzzy extensions of these methods are preferred because of the complexity of the green supplier selection problem and inclusion of both quantitative and qualitative criteria. Also, these criteria may be uncertain and conflict with each other. It is the first time that SWARA-F is combined with MARCOS-F for the green supplier assessment and selection of the best one among them. The effectiveness of the proposed methodology is demonstrated by solving the real selection problem of a company from textile industry. In the problem both classic and green criteria including main and sub-criteria are considered. SWARA-F is used for weighting the evaluation criteria and the rank of each green supplier alternatives is obtained from incomplete information by assessment score calculated from MARCOS-F. The effectiveness of the combination of two methods is verified by sensitivity and comparative analyses. The proposed methodology provides acceptable and satisfactory results in determining the best green supplier namely improving the environmental and cost efficiency evaluation process.

Keywords

Multi criteria decision making, SWARA-F, MARCOS-F, Green supplier selection

References

• [1] Quan, J., Zeng, B. and Liu, D., “Green supplier selection for process industries using weighted grey incidence decision model”, Complexity, 2018: 1-12, (2018).
• [2] Christopher, M., “Logistics & Supply Chain Management”, 5th Edition, UK: Pearson, (2016).
• [3] Ketikidis, P.H., Koh, S.C.L, Dimitriadis, N., Gunasekarand, A. and Kehajova, M., “The use of information systems for logistics and supply chain management in South East Europe: Current status and future direction”, Omega, 36: 592-599, (2008).
• [4] Buyukozkan, G. and Cifci, G., “A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers”, Expert Systems with Applications, 39(3): 3000-3011, (2012).
• [5] Zafar, A., Zafar, M., Sarwar, A., Raza, H. and Khan, M.T., “A Fuzzy AHP method for green supplier selection and evaluation”, Proceedings of the 12th International Conference on Management Science and Engineering Management, Australia, 1355-1366, (2019).
• [6] Kannan, D., Govindan, K., Rajendran, S., “Fuzzy axiomatic design approach based green supplier selection: A case study from Singapore”, Journal of Cleaner Production, 96: 194-208, (2015).
• [7] Galankashi, M.R., Chegeni, A., Soleimanynanadegany, A., Memari, A., Anjomshoae, A., Helmi, S.A. and Dargi, A., “Prioritizing green supplier selection criteria using fuzzy analytical network process”, Procedia CIRP, 26: 689-694, (2015).
• [8] Chaharsooghi, S.K. and Ashrafi, M., “Sustainable supplier performance evaluation and selection with neofuzzy TOPSIS method”, International Scholarly Research Notices, 434168: 1-10, (2014).
• [9] Chin, T.A., Tat, H.H. and Sulaiman, Z., “Green supply chain management, environmental collaboration and sustainability performance”, Procedia CIRP, 26: 695-699, (2015).
• [10] Arshadi Khamseh, A. and Mahmoodi, M., “A new fuzzy TOPSIS-TODIM hybrid method for green supplier selection using fuzzy time function”, Advances in Fuzzy Systems, 841405: 1-10, (2014).
• [11] Zadeh, L.A., “Fuzzy sets”, Information and Control, 8: 338-353, (1965).
• [12] Mardani, A., Nilashi, .M, Zakuan, N., Loganathan, N., Soheilirad, S., Saman, M.Z.M. and Ibrahim, O., “A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments”, Applied Soft Computing, 57: 265-292, (2017).
• [13] Stević, Ž., Pamučar, D., Puška, A. and Chatterjee, P., “Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to Compromise solution (MARCOS)”, Computers & Industrial Engineering, 140: 106231, (2020).
• [14] Bali, O., Kose, E. and Gumus S., “Green supplier selection based on IFS and GRA”, Grey Systems: Theory and Application, 3(2): 158-176, (2013).
• [15] Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I.E. and Omid, M., “Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry”, Computers & Operations Research, 89: 337-347, (2018).
• [16] Sarwar, A., Xu, J., Zeng, Z. and Hashim, M., “AHP and fuzzy TOPSIS methods for green supplier selection and evaluation”, International Journal of Advanced Research, 5(3): 625-635, (2017).
• [17] Sun, Y. and Cai, Y., “A flexible decision-making method for green supplier selection integrating TOPSIS and GRA under the single-valued neutrosophic environment”, IEEE Access, 9: 83025-83040, (2021).
• [18] Tuzkaya, G., Ozgen, A., Ozgen, D. and Tuzkaya, U.R., “Environmental performance evaluation of suppliers: a hybrid fuzzy multi-criteria decision approach”, International journal of Environmental Science and Technology, 6: 477-490, (2009).
• [19] Gao, H., Ju, Y., Gonzalez, E.D.S. and Zhang, W., “Green supplier selection in electronics manufacturing: An approach based on consensus decision making”, Journal of Cleaner Production, 245: 118781, (2020).
• [20] Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A. and Diabat, A., “Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain”, Journal of Cleaner Production, 47: 355-367, (2013).
• [21] Mohammed, A., Harris, I., Soroka, A., Naim, M.M. and Ramjaun, T., “Evaluating green and resilient supplier performance: AHP-fuzzy TOPSIS decision-making approach”, Proceedings of the 7th International Conference on Operations Research and Enterprise Systems (ICORES), Portugal, 209-216, (2018).
• [22] Qin, J., Liu, X. and Pedrycz, W., “An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment”, European Journal of Operational Research, 258(2): 626-638, (2017).
• [23] Xu, D., Cui, X. and Xian, H., “An extended EDAS method with a single-valued complex neutrosophic set and its application in green supplier selection”, Mathematics, 8(2): 282, (2020).
• [24] Javad, M.O.M., Darvishi, M. and Javad, A.O.M., “Green supplier selection for the steel industry using BWM and fuzzy TOPSIS: A case study of Khouzestan steel company”, Sustainable Futures, 2, 100012, (2020).
• [25] Akcan, S. and Taş, M. A., “Green supplier evaluation with SWARA-TOPSIS integrated method to reduce ecological risk factors”, Environmental Monitoring and Assessment, 191(12): 1-22, (2019).
• [26] Yazdani, M., Hashemkhani Zolfani, S. and Zavadskas, E. K., “New integration of MCDM methods and QFD in the selection of green suppliers”, Journal of Business Economics and Management, 17(6): 1097-1113, (2016).
• [27] Duan, C. Y., Liu, H. C., Zhang, L. J. and Shi, H., “An extended alternative queuing method with linguistic Z-numbers and its application for green supplier selection and order allocation”, International Journal of Fuzzy Systems, 21(8): 2510-2523, (2019).
• [28] Dogan, A., Soylemez, I. and Ozcan, U., “Green supplier selection by using fuzzy Topsis method”, Uncertainty Modelling in Knowledge Engineering and Decision Making: Proceedings of the 12th International FLINS Conference, France, 638-645, (2016).
• [29] Mousakhani, S., Nazari-Shirkouhi, S. and Bozorgi-Amiri, A., “A novel interval type-2 fuzzy evaluation model based group decision analysis for green supplier selection problems: A case study of battery industry”, Journal of Cleaner Production, 168: 205-218, (2017).
• [30] Yucesan, M., Mete, S., Serin, F., Celik, E. and Gul, M., “An integrated best-worst and interval type-2 fuzzy TOPSIS methodology for green supplier selection”, Mathematics, 7: 182, (2019).
• [31] Rouyendegh, B.D., Yildizbasi, A. and, Üstünyer, P., “Intuitionistic fuzzy TOPSIS method for green supplier selection problem”, Soft Computing, 24(3): 2215-2228, (2020).
• [32] Vishnu, V.A., Babu, J. and George, B., “Green supplier selection using hybrid grey relational analysis with fuzzy logic method”, IOP Conference Series: Materials Science and Engineering, IOP Publishing, 396(1): 012073, (2018).
• [33] Ecer, F., “Multi-criteria decision making for green supplier selection using interval type-2 fuzzy AHP: a case study of a home appliance manufacturer”, Operational Research, 1-35, (2020).
• [34] Mina, H., Mirabedin, S.N. and Pakzad-Moghadam, S.H., “An integrated fuzzy analytic network process approach for green supplier selection: A case study of petrochemical industry”, Journal of Management Science and Practice, 2(2): 31-47, (2014).
• [35] Ustunyer, P., Eraslan, E., Erdebilli, B. and Yildizbas, A., “Intuitionistic fuzzy ELECTRE method for green supplier selection problem”, Proceedings of the 11th International NCM Conferences, Ankara, 89, (2018).
• [36] Yu, M.C. and Su, M.H., “Using fuzzy DEA for green suppliers selection considering carbon footprints”, Sustainability, 9(4): 495, (2017).
• [37] Sen, D.K., Datta, S., Patel, S.K. and Mahapatra, S.S., “Green supplier selection in fuzzy context: a decision-making scenario on application of fuzzy-MULTIMOORA”, International Journal of Services and Operations Management, 28(1): 98-140, (2017).
• [38] Beng, L.G. and Badrul, O., “Green supplier selection: analysis of qualitative environmental criteria using fuzzy axiomatic approach”, Applied Mechanics and Materials, 465: 1054-1059, (2014).
• [39] Guo, Z., Liu, H., Zhang, D. and Yang, J., “Green supplier evaluation and selection in apparel manufacturing using a fuzzy multi-criteria decision-making approach”, Sustainability, 9(4): 650, (2017).
• [40] Sang, X. and Liu, X., “An interval type-2 fuzzy sets-based TODIM method and its application to green supplier selection”, Journal of the Operational Research Society, 67(5): 722-734, (2016).
• [41] Keshavarz Ghorabaee, M., Zavadskas, E.K., Amiri, M. and Esmaeili, A., “Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets”, Journal of Cleaner Production, 137: 213-229, (2016).
• [42] Mishra, A.R., Rani, P., Pardasani, K.R. and Mardani, A., “A novel hesitant fuzzy WASPAS method for assessment of green supplier problem based on exponential information measures”, Journal of Cleaner Production, 238: 117901, (2019).
• [43] Kumari, R. and Mishra, A.R., “Multi-criteria COPRAS method based on parametric measures for intuitionistic fuzzy sets: application of green supplier selection”, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 44(4): 1645-1662, (2020).
• [44] Shaw, K., Shankar, R., Yadav, S.S. and Thakur, L.S., “Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain”, Expert Systems with Applications, 39(9): 8182-8192, (2012).
• [45] Mavi, R.K., “Green supplier selection: a fuzzy AHP and fuzzy ARAS approach”, International Journal of Services and Operations Management, 22(2): 165-188, (2015).
• [46] Gavareshki, M.H.K., Hosseini, S.J. and Khajezadeh, M., “A case study of green supplier selection method using an integrated ISM-Fuzzy MICMAC analysis and multi-criteria decision making”, International Journal of Management Science and Engineering,16(4): 562-573, (2017).
• [47] Lo, H.W., Liou, J.J., Wang, H.S. and Tsai, Y.S., “An integrated model for solving problems in green supplier selection and order allocation”, Journal of Cleaner Production, 190: 339-352, (2018).
• [48] Gupta, S., Soni, U. and Kumar, G., “Green supplier selection using multi-criterion decision making under fuzzy environment: A case study in automotive industry”, Computers & Industrial Engineering, 136: 663-680, (2019).
• [49] Đalić, I., Stević, Ž., Karamasa, C. and Puška, A., “A novel integrated fuzzy PIPRECIA-interval rough SAW model: Green supplier selection”, Decision Making: Applications in Management and Engineering, 3(1): 126-145, (2020).
• [50] Qu, G., Zhang, Z., Qu, W. and Xu, Z., “Green supplier selection based on green practices evaluated using fuzzy approaches of TOPSIS and ELECTRE with a case study in a Chinese internet company”, Journal of Environmental and Public Health, 17(9): 3268, (2020).
• [51] Liao, H. C., Ren, R. X., Antucheviciene, J., Šaparauskas, J. and Al-Barakati, A., “Sustainable construction supplier selection by a multiple criteria decision-making method with hesitant linguistic information”, Economics and Management, 23(4): 119-136, (2020).
• [52] Zimmermann, H.J., “Fuzzy Set Theory and its Applications”, 2nd Edition, Boston: Kluwer Academic Publishers, 57, (1992).
• [53] Kaufmann, A. and Gupta, M.M., “Fuzzy Mathematical Models in Engineering and Management Science”, 1st Edition, Amsterdam: Elsevier Science Publishers B.V., 28, 56-60, (1988).
• [54] Maheswari, S., Shalini, M. and Yookesh, T.L., “Defuzzification formula for modelling and scheduling a furniture fuzzy project network”, International Journal of Engineering and Advanced Technology, 9 (1S5): 279-283, (2019).
• [55] Chen, S.H. and Hsieh, C.H., “Representation, ranking, distance, and similarity of L-R type fuzzy number and application”, Australian Journal of Intelligent Information Processing Systems, 6 (4): 217-229, (2000).
• [56] Chou, T.Y., Hsu, C.L. and Chen, M.C., “A fuzzy multi-criteria decision model for international tourist hotels location selection”, International Journal of Hospitality Management, 27(2): 293-301, (2008).
• [57] Keršuliene, V., Zavadskas, E.K. and Turskis, Z., “Selection of rational dispute resolution method by applying new stepwise weight assessment ratio analysis (SWARA)”, Journal of Business Economics and Management, 11(2): 243–258, (2010).
• [58] Zolfani, S.H. and Saparauskas, J., “New application of SWARA method in prioritizing sustainability assessment indicators of energy system”, Engineering economics, 24(5): 408-414, (2013).
• [59] Zolfani, S.H. and Zavadskas, E.K., “Sustainable development of rural areas’ building structures based on local climate”, Procedia Engineering, 57: 1295-1301, (2013).
• [60] Zolfani, S.H. and Banihashemi, S.S.A., “Personnel selection based on a novel model of game theory and MCDM approaches”, Proceedings of the 8th International Scientific Conference: Business and Management, Lithuania, 191-198, (2014).
• [61] Percin, S., “An integrated fuzzy SWARA and fuzzy AD approach for outsourcing provider selection”, Journal of Manufacturing Technology Management, 30(2): 531-552, (2019).
• [62] Mavi, R.K., Goh, M. and Zarbakhshnia, N., “Sustainable third-party reverse logistic provider selection with fuzzy SWARA and fuzzy MOORA in plastic industry”, The International Journal of Advanced Manufacturing, 91(5-8): 2401-2418, (2017).
• [63] Ulutas, A., “University website performance evaluation using fuzzy SWARA and WASPAS-F”, In Book Multi-Criteria Decision-Making Models for Website Evaluation, 151-165, IGI Global, (2019).
• [64] Agarwal, S., Kant, R. and Shankar, R., “Evaluating solutions to overcome humanitarian supply chain management barriers: A hybrid fuzzy SWARA–fuzzy WASPAS approach”, International Journal of Disaster Risk Reduction, 51, 101838, (2020).
• [65] Zarbakhshnia, N., Soleimani, H. and Ghaderi, H., “Sustainable third-party reverse logistics provider evaluation and selection using fuzzy SWARA and developed fuzzy COPRAS in the presence of risk criteria”, Applied Soft Computing, 65: 307-319, (2018).
• [66] Ansari, Z.N, Kant, R. and Shankar, R., “Evaluation and ranking of solutions to mitigate sustainable remanufacturing supply chain risks: a hybrid fuzzy SWARA-fuzzy COPRAS framework approach”, International Journal of Sustainable Engineering, 13(6): 473-494, (2020).
• [67] Ren, R.X., Liao, H.C., Al-Barakati, A., Cavallaro, F., “Electric vehicle charging station site selection by an integrated hesitant fuzzy SWARA-WASPAS Method”, Transformations in Business & Economics, 18(2): 103-123, (2019).
• [68] Petrović, G., Mihajlović, J., Ćojbašić, Ž., Madić, M. and Marinković, D., “Comparison of three fuzzy MCDM methods for solving the supplier selection problem”, Facta Universitatis, Series: Mechanical Engineering, 17(3): 455-469, (2019).
• [69] Ulutas, A., “Using of fuzzy SWARA and fuzzy ARAS methods to solve supplier selection problem”, In Theoretical and Applied Mathematics in International Business, 136-148, IGI Global, (2020).
• [70] Rani, P., Mishra, A.R., Krishankumar, R., Mardani, A., Cavallaro, F., Soundarapandian Ravichandran, K. and Balasubramanian, K., “Hesitant fuzzy SWARA-complex proportional assessment approach for sustainable supplier selection (HF-SWARA-COPRAS)”, Symmetry, 12(7): 1152, (2020).
• [71] Mishra, A.R., Rani, P., Pandey, K., Mardani, A., Streimikis, J., Streimikiene, D. and Alrasheedi, M., “Novel multicriteria intuitionistic fuzzy SWARA–COPRAS approach for sustainability evaluation of the bioenergy production process”, Sustainability, 12(10): 4155, (2020).
• [72] Ulutas, A., Karakus, C.B. and Topal, A., “Location selection for logistics center with fuzzy SWARA and CoCoSo methods”, Journal of Intelligent & Fuzzy Systems, 38(4): 4693-4709, (2020).
• [73] Ghasemi, P., Mehdiabadi, A., Spulbar, C. and Birau, R., “Ranking of sustainable medical tourism destinations in Iran: An integrated approach using fuzzy SWARA-PROMETHEE”, Sustainability, 13(2): 683, (2021).
• [74] Chang, D.Y., “Applications of the extent analysis method on fuzzy AHP”, European Journal of Operational Research, 95(3): 649-655, (1996).
• [75] Stanković, M., Stević, Ž., Das, D.K., Subotić, M. and Pamučar, D., “A new fuzzy MARCOS method for road traffic risk analysis”, Mathematics, 8(3): 457, (2020).
• [76] Stević, Ž. and Brković, N., “A novel integrated FUCOM-MARCOS model for evaluation of human resources in a transport company”, Logistics, 4(1): 4, (2020).
• [77] Puška, A., Stojanović, I., Maksimović, A. and Osmanović, N., “Evaluation software of project management used measurement of alternatives and ranking according to compromise solution (MARCOS) method”, Operational Research in Engineering Sciences: Theory and Applications, 3(1): 89-102, (2020).
• [78] Ulutas, A., Karabasevic, D., Popovic, G., Stanujkic, D., Nguyen, P. T. and Karakoy, Ç., “Development of a novel integrated CCSD-ITARA-MARCOS decision-making approach for stackers selection in a logistics system”, Mathematics, 8(10): 1672, (2020).
• [79] Biswas, S., “Measuring performance of healthcare supply chains in India: A comparative analysis of multi-criteria decision making methods”, Decision Making: Applications in Management and Engineering, 3(2): 162-189, (2020).
• [80] Ilieva, G., Yankova, T., Hadjieva, V., Doneva, R. and Totkov, G., “Cloud service selection as a fuzzy multi-criteria problem”, TEM Journal, 9 (2): 484-495, (2020).
• [81] Mitrović Simić, J., Stević, Ž., Zavadskas, E.K., Bogdanović, V., Subotić, M. and Mardani, A., “A novel CRITIC-Fuzzy FUCOM-DEA-Fuzzy MARCOS model for safety vvaluation of road sections based on geometric parameters of road”, Symmetry, 12(12): 2006, (2020).
• [82] Simić, V., Soušek, R. and Jovčić, S., “Picture fuzzy MCDM approach for risk assessment of railway infrastructure”, Mathematics, 8(12): 2259, (2020).
• [83] Taş, M. A. and Çakır, E., Ranking Triangular and Trapezoidal Fuzzy MARCOS Methods on Road Risk, In 2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI), 370-375, IEEE, (2021).
• [84] Pamucar, D., Iordache, M., Deveci, M., Schitea, D. and Iordache, I., “A new hybrid fuzzy multi-criteria decision methodology model for prioritizing the alternatives of the hydrogen bus development: A case study from Romania”, International Journal of Hydrogen Energy, 46(57): 29616-29637, (2021).
• [85] Bakır, M. and Atalık, Ö., “Application of fuzzy AHP and fuzzy MARCOS approach for the evaluation of e-service quality in the airline industry”, Decision Making: Applications in Management and Engineering, 4(1): 127-152, (2021).
• [86] Puška, A., Stević, Ž. and Stojanović, I., “Selection of sustainable suppliers using the fuzzy MARCOS method”, Current Chinese Science, 1(2): 218-229, (2021).
• [87] Tuş, A., Aytaç Adalı, E., “İnternet servis sağlayıcı seçim probleminin çözümünde bulanık sıralama ağırlık tabanlı bulanık MARCOS yöntemi”, Politeknik Dergisi, 1-1, DOI: 10.2339/politeknik.881609, (2021).
• [88] Ecer, F. and Pamucar, D., “MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services”, Applied Soft Computing, 104, 107199, (2021).
• [89] Celik, E. and Gul, M., “Hazard identification, risk assessment and control for dam construction safety using an integrated BWM and MARCOS approach under interval type-2 fuzzy sets environment”, Automation in Construction, 127: 103699 (2021).
• [90] Gong, X., Yang, M. and Du, P., “Renewable energy accommodation potential evaluation of distribution network: A hybrid decision-making framework under interval type-2 fuzzy environment”, Journal of Cleaner Production, 286: 124918, (2021).
• [91] Ali, J., “A novel score function based CRITIC-MARCOS method with spherical fuzzy information”, Computational and Applied Mathematics, 40(8): 1-27, (2021).
• [92] Pamucar, D., Ecer, F. and Deveci, M., “Assessment of alternative fuel vehicles for sustainable road transportation of United States using integrated fuzzy FUCOM and neutrosophic fuzzy MARCOS methodology”, Science of The Total Environment, 788, 147763, (2021).
• [93] Deveci, M., Özcan, E., John, R., Pamucar, D. and Karaman, H., “Offshore wind farm site selection using interval rough numbers based Best Worst Method and MARCOS”, Applied Soft Computing, 107532, (2021).
• [94] Badi, I. and Pamucar, D., “Supplier selection for steelmaking company by using combined Grey-MARCOS methods”, Decision Making: Applications in Management and Engineering, 3(2): 37-48, (2020).
• [95] Torkayesh, A. E., Zolfani, S. H., Kahvand, M. and Khazaelpour, P., “Landfill location selection for healthcare waste of urban areas using hybrid BWM-grey MARCOS model based on GIS”, Sustainable Cities and Society, 67, 102712, (2021).
• [96] Chakraborty, S., Chattopadhyay, R. and Chakraborty, S., “An integrated D-MARCOS method for supplier selection in an iron and steel industry”, Decision Making: Applications in Management and Engineering, 3(2): 49-69, (2020).
• [97] Santos, F.J.J. and Camargo, H.A., “Fuzzy systems for multicriteria decision making”, Clei Electronic Journal, 13(3): 1-9, (2010).
• [98] Keshavarz Ghorabaee, M., Zavadskas, E.K., Turskis, Z. and Antucheviciene, J., “A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making”, Economic Computation & Economic Cybernetics Studies & Research, 50(3): 25-44, (2016).
• [99] Hwang, C.L. and Yoon, K., “Multiple attribute decision-making: Methods and Application”, 1st Edition, New York: Springer, 128-141, (1981).
• [100] Keshavarz Ghorabaee, M., Zavadskas, E.K., Olfat, L. and Turskis, Z., “Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS)”, Informatica, 26(3): 435-451, (2015).
• [101] Aytac Adali, E. and Tus, A., “Hospital site selection with distance-based multi-criteria decision-making methods”, International Journal of Healthcare Management, 14 (2): 534-544, (2021).
• [102] Chen, C.T., “Extensions of the TOPSIS for group decision-making under fuzzy environment”, Fuzzy Sets and Systems, 114(1): 1-9, (2000).
• [103] Keshavarz Ghorabaee, M., Zavadskas, E.K., Amiri, M. and Turskis, Z., “Extended EDAS method for fuzzy multi-criteria decision-making: an application to supplier selection”, International Journal of Computers Communications & Control, 11(3): 358-371, (2016).
• [104] Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E.K., Hooshmand, R. and Antuchevičienė, J., “Fuzzy extension of the CODAS method for multi-criteria market segment evaluation”, Journal of Business Economics and Management, 18(1): 1-19, (2017).