A new integrated fuzzy MCDM approach and its application to wastewater management

This paper proposes a fuzzy multi-criteria group decision making methodology that combines 2-tuple fuzzy linguistic representation model, linguistic hierarchies, Decision Making Trial and Evaluation Laboratory (DEMATEL) method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The multi-granular linguistic information obtained from decision-makers are unified and aggregated employing linguistic hierarchies and 2-tuple fuzzy linguistic representation model. The weights of the criteria are calculated employing DEMATEL method, which enables to consider inner dependencies among criteria. Then, fuzzy TOPSIS method is utilized to rank the alternatives. The developed methodology is able to handle information in a decision making problem with multiple information sources. Furthermore, it enables managers to deal with heterogeneous information without loss of information. The developed methodology is used to determine the most suitable wastewater treatment (WWT) alternative for Istanbul, the largest city of Turkey that is also listed among the world's most crowded cities

___

[1] N. Brunner, and M. Starkl, “Financial and economic determinants of collective action: The case of wastewater management,” Environmental Impact Assessment Review, vol. 31, pp. 140-150, 2012.

[2] S.E. Uwadiae, Y. Yerima, and R.U. Azike, “Enzymatic biodegradation of pharmaceutical wastewater,” International Journal of Energy and Environment, vol. 2, no. 4, pp. 683- 690, 2011.

[3] P.P. Kalbar, S. Karmakan, and S.R. Asolekar, “Selection of an appropriate wastewater treatment technology: A scenariobased multiple-attribute decision-making approach,” J. Environ. Manage., vol. 113, pp. 158-169, 2012.

[4] G. Tchobanoglous, and F.L. Burton, “Wastewater Engineering: Treatment, Disposal, and Reuse,” Metclef & Eddy Inc., 1991.

[5] S.J. Arceivala and S.L. Asolekar, “Wastewater treatment for pollution control and reuse,” McGraw Hill, New Delhi, 2007.

[6] Republic of Turkey Turkish Statistical Institute, “Municipal Wastewater Statistics,” 2010. Available at: http://www.turkstat.gov.tr/PreHaberBultenleri.do?id=10752.

[7] F. Herrera, and L. Martínez, “An approach for combining linguistic and numerical information based on 2-tuple fuzzy representation model in decision-making,” Int. J. Uncertain. Fuzz., vol. 8, no. 5, pp. 539-562, 2000 .

[8] M. Zeleny M, “Multiple criteria decision making,” New York, McGraw-Hill, 1982.

[9] K.P. Yoon, C.L. Hwang, “Multiple attribute decision making: An introduction,” Sage University Papers series on Quantitative Applications, 07-104, Thousand Oaks, 1995.

[10] The London School of Economics and Political Science, “Multi-criteria Decision Analysis”, 2007. Available at: http://www.lse.ac.uk/collections/summerSchool/courseoutline s/management/Multi-criteria%20analysis%20manual.htm, (2007).

[11] E.K. Zavadskas, Z. Turskis, and S. Kildienė, “State of art surveys of overviews on MCDM/MADM methods,” Technological and Economic Development of Economy, vol. 20, pp. 165–179, 2014.

[12] L. Gigovic, D. Pamucar, D. Bozanic et al., “Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: A case study of Vojvodina, Serbia,” Renewable Energy, vol. 103, pp. 501-521, 2017.

[13] H. Gupta and M.K. Barua, “Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS,” Journal of Cleaner Production, vol. 152, pp. 242-258, 2017.

[14] R. Nie, J. Wang, and H. Zhang, “Solving Solar-Wind Power Station Location Problem Using an Extended Weighted Aggregated Sum Product Assessment (WASPAS) Technique with Interval Neutrosophic Sets,” Symmetry-Basel, vol. 9, no. 7, pp. 106, 2017.

[15] S.H. Mousavi-Nasab and S.A. Alireza, “A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems,” Materials & Design, vol. 121, pp. 237-253, 2017.

[16] X. Peng and J. Dai, “Hesitant fuzzy soft decision making methods based on WASPAS, MABAC and COPRAS with combined weights,” Journal of Intelligent & Fuzzy Systems, vol. 33, no. 2, pp. 1313-1325, 2017.

[17] K. Rathi and B. Balamohan, “A mathematical model for subjective evaluation of alternatives in fuzzy multi-criteria group decision making using COPRAS method,” International Journal of Fuzzy Systems, vol. 19, no. 5, pp. 1290-1299, 2017.

[18] N. Salimi, “Quality assessment of scientific outputs using the BWM,” Scientometrics, vol. 112, no. 1, pp. 195- 213, 2017.

[19] D. Stanujkic, E.K. Zavadskas, M.K. Ghorabaee, et al., “An extension of the EDAS method based on the use of interval grey numbers,” Studies in Informatics and Control, vol. 26, no. 1, pp. 5-12, 2017.

[20] S. Urosevic, D. Karabasevic, D. Stanujkic, et al., “An approach to personnel selection in the tourism industry based on the SWARA and the WASPAS methods,” Economic Computation And Economic Cybernetics Studies and Research, vol. 51, no. 1, pp. 75-88, 2017.

[21] L. J. Gigovic, D. Pamucar, Z. Bajic, et al., “The combination of expert judgment and GIS-MAIRCA analysis for the selection of sites for ammunition depots,” Sustainability, vol. 8, no. 4, pp. 372, 2016.

[22] G.M. Keshavarz, E.K. Zavadskas, M. Amiri, et al., “Extended EDAS Method for Fuzzy Multi-criteria Decisionmaking: An Application to Supplier Selection,” International Journal of Computers Communications & Control, vol. 11, no. 3, pp. 358-371, 2016.

[23] J.J.H. Liou, J. Tamosaitiene, E.K. Zavadskas, et al., “New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management,” International Journal of Production Research, vol. 54, no. 1, pp. 114-134, 2016.

[24] E. Turanoglu Bekar, M. Cakmakci, and C. Kahraman, “Fuzzy COPRAS method for performance measurement in total productive maintenance: a comparative analysis,” Journal of Business Economics and Management, vol. 17, no. 5, pp. 663-684, 2016.

[25] Y.X. Xue, J.X. You, X.D. Lai, et al., “An intervalvalued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information,” Applied Soft Computing, vol. 38, pp. 703-713, 2016.

[26] Q. Yang, Z. Zhang, X. You, et al., “Evaluation and classification of overseas talents in China based on the BWM for intuitionistic relations,” Symmetry-Basel, vol. 8, no. 11, pp. 137, 2016.

[27] E.K. Zavadskas, D. Kalibatas, and D. Kalibatiene, “A multi-attribute assessment using WASPAS for choosing an optimal indoor environment,” Archives of Civil and Mechanical Engineering, vol. 16, no. 1, pp. 76-85, 2016.

[28] H.T. Nguyen, S.Z.M. Dawal, Y. Nukman, et al., “An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation,” PLOS ONE, vol. 10, no. 9, 2015.

[29] M.A. Makhesana, “Application of improved complex proportional assessment (COPRAS) method for rapid prototyping system selection,” Rapid Prototyping Journal, vol. 21, no. 6, pp. 671-674, 2015.

[30] D. Pamucar, G. Cirovic, “The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC),” Expert Systems with Applications, vol. 42, no. 6, pp. 3016-3028, 2015.

[31] Z. Turskis, E.K. Zavadskas, J. Antucheviciene, et al., “A Hybrid Model Based on Fuzzy AHP and Fuzzy WASPAS for Construction Site Selection,” International Journal of Computers Communications & Control, vol. 10, no. 6, pp. 873-888, 2015.

[32] R. Chauhan, T. Singh, A. Tiwari, et al., “Hybrid entropy - TOPSIS approach for energy performance prioritization in a rectangular channel employing impinging air jets,” Energy, vol. 134, pp. 360-368, 2017.

[33] U.P. Onu, Q. Xie, L. Xu, “A Fuzzy TOPSIS model Framework for Ranking Sustainable Water Supply Alternatives,” Water Resources Management, vol. 31, no. 9, pp. 2579-2593, 2017.

[34] D. Pamucar, M. Mihajlovic, R. Obradovic, et al., “Novel approach to group multi-criteria decision making based on interval rough numbers: HybridDEMATEL-ANPMAIRCA model,” Expert Systems with Applications, vol. 88, pp. 58-80, 2017.

[35] S. Dimic, D. Pamucar, S. Ljubojevic, B.Dorovic, “Strategic transport management models-The case dtudy of an oil industry,” Sustainability, vol. 8, no. 9, pp. 954, 2016.

[36] S.H. Zyoud, L.G. Kaufmann, G. Lorenz, H. Shaheen, et al., “A framework for water loss management in developing countries under fuzzy environment: Integration of Fuzzy AHP with Fuzzy TOPSIS,” Expert Systems with Applications, vol. 61, pp. 86-105, 2016.

[37] M. Alemi, M. Kalbasi, F. Rashidi, “Improvement of Oil Production Rate Using the TOPSIS and VIKOR Computer Mathematical Models,” Oil Gas-European Magazine, vol. 41, no. 4, pp. 205-209, 2015.

[38] L. J. Gigovic, D. Pamucar, D. Lukic, et al., “GIS-Fuzzy DEMATEL MCDA model for the evaluation of the sites for ecotourism development: A case study of "Dunayski kljuc" region, Serbia,” Land Use Policy, vol. 58, pp. 348-365, 2015.

[39] P. Aragonés-Beltrán, J.A. Mendoza-Roca, A. Bes-Piá, M. García-Melón, and E. Parra-Ruiz, “Application of multicriteria decision analysis to jar-test results for chemicals selection in the physical–chemical treatment of textile wastewater,” J. Hazard. Mater. vol., 164, pp. 288-29, 2009.

[40] M. Bottero, E. Comino, and V. Riggio, “Application of the analytic hierarchy process and the analytic network process for the assessment of different wastewater treatment systems,” Environ. Modell. Softw., vol. 26, pp. 1211-1224, 2011.

[41] A.R. Karimi, N. Mehrdadi, S.J. Hashemian, G.R. Nabi Bidhendi, and R. Tavakkoli Moghaddam, “Selection of wastewater treatment process based on the analytical hierarchy process and fuzzy analytical hierarchy process methods,” Int. J. Environ. Sci. Tech., vol. 8, no. 2, pp. 267- 280, 2011.

[42] R. Sala-Garridoa, M. Molinos-Senante, and F. Hernández-Sancho, “Comparing the efficiency of wastewater treatment technologies through a DEA metafrontier model,” Chem. Eng. J., vol. 173, pp. 766– 772, 2011.

[43] Z. Srdjevic, M. Samardzic, and B. Srdjevic, “Robustness of AHP in selecting wastewater treatment method for the coloured metal industry: Serbian case study,” Civ. Eng. Environ. Syst., vol. 29, no. 2, pp. 147-161, 2012.

[44] P.P. Kalbar, S. Karmakan, and S.R. Asolekar, “The influence of expert opinions on the selection of wastewater treatment alternatives: A group decision-making approach,” J. Environ. Manage., vol. 128, pp. 844-851, 2013.

[45] G.X. Gao, and Z.P. Fan, “MADM method considering attribute aspirations with an application to selection of wastewater treatment Technologies,” Kybernetes, vol. 44, no. 5, pp. 739-756, 2015.

[46] P.P. Kalbar, S. Karmakan, and S.R. Asolekar, “Selection of wastewater treatment alternative: Significance of choosing MADM method,” Environ. Eng. Manag. J., vol. 14, no. 5, pp. 1011-1020, 2015.

[47] X. Ouyang, F. Guo, D. Shan, H. Yu, and J. Wang, “Development of the integrated fuzzy analytical hierarchy process with multidimensional scaling in selection of natural wastewater treatment alternatives,” Ecol. Eng., vol. 74, pp. 438-447, 2015.

[48] M. Molinos-Senante, F. Hernandez-Sancho, and R. Sala-Garrido, “Comparing the dynamic performance of wastewater treatment systems: A metafrontier Malmquist productivity index approach,” J. Environ. Manage., vol. 161, pp. 309-316, 2015.

[49] A. Castillo, P. Vall, M. Garrido-Baserba , J. Comas, and M. Poch, “Selection of industrial (food, drink and milk sector) wastewater treatment technologies: A multi-criteria assessment,” Journal of Cleaner Production, vol. 143, pp. 180-190, 2017.

[50] Y. Li, Y. Hub, X. Zhanga, Y. Denga, and S. Mahadevanc, “An evidential DEMATEL method to identify critical success factors in emergency management,” Applied Soft Computing, vol. 22, pp. 504-510, 2014.

[51] F.J. Estrella, M. Espinilla, F. Herrera, and L. Martínez, “FLINTSTONES: A fuzzy linguistic decision tools enhancement suite based on the 2-tuple linguistic model and extensions,” Inform. Sciences, vol. 280, pp. 152-170, 2014.

[52] C. Rao, J. Zheng, C. Wang, and X. Xiao, “A hybrid multi-attribute group decision making method based on grey linguistic 2-tuple,” Iranian Journal of Fuzzy Systems, vol. 13, no. 2, pp. 37-59, 2016.

[53] F. Herrera, and L. Martínez, “A 2-tuple fuzzy linguistic representation model for computing with words,” IEEE T. Fuzzy. Syst., vol. 8, no. 6, pp. 746-752, 2000.

[54] O. Cordon, and F. Herrera, I. Zwir, “Linguistic modeling by hierarchical systems of linguistic rules,” IEEE T. Fuzzy. Syst., vol. 10, no. 1, pp. 2-20, 2002.

[55] F. Herrera, and L. Martínez, “A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making,” IEEE T. Syst. Man Cy. B., vol. 31, no. 2, pp. 227-234, 2001.

[56] F. Herrera, E. Herrera-Viedma, and L. Martinez, “A fuzzy linguistic methodology to deal with unbalanced linguistic term sets,” IEEE T. Fuzzy. Syst., vol. 16, no. 2, pp. 354-370, 2008.

[57] Y.B. Li, and J.P. Zhang, “TOPSIS method for hybrid multiple attribute decision making with 2-tuple linguistic information and its application to computer network security evaluation,” Journal of Intelligent & Fuzzy Systems, vol. 26, pp. 1563-1569, 2014.

[58] V.N. Huynh, C.H. Nguyen, and Y. Nakamori, “MEDM in general multi-granular hierarchical linguistic contexts based on the 2-tuples linguistic model, Granular Computing,” 2005 IEEE International Conference on, vol. 2, pp. 482-487, 2005.