Abstract
Public and private companies make significant water infrastructure investments to meet increasing water demand. In this context, investments in wastewater treatment plants (WWTPs), which play an important role in recycling of used water, are also increasing. This study investigates determination of the efficiency scores of WWTPs considering each metropolitan municipality as a decision-making unit (DMU). In this study, a two-step methodology is established to determine efficiency scores of WWTPs. In the first step, the input and output parameters are searched by a literature review for the performance evaluation, and candidate parameters are determined. Then, to determine the most appropriate and related parameters, the importance weights of all candidate inputs and outputs are computed using the extended stepwise weight assessment ratio analysis (SWARA) method. Next, the inputs and outputs are chosen according to their importance weights. In the second step, efficiency scores of WWTPs are calculated using output-oriented data envelopment analysis (DEA) models. Based on the expert opinions, the parameters used as input variables are as follows: Daily Wastewater Amount per Person Discharged in Municipalities, WWTP Capacity, and Number of WWTPs; and the parameters used as output variables are as follows; Amount of Wastewater Treated in WWTPs and Municipal Population Served by WWTPs. The results are presented and discussed by sensitivity analysis. Results show that 14 metropolitan municipalities have total efficiency, 19 metropolitan municipalities have technical efficiency, and 21 metropolitan municipalities have scale efficiency.
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References
Akhanova G, Nadeem A, Kim JR, Azhar S (2020) A multi-criteria decision-making framework for building sustainability assessment in Kazakhstan. Sustain Cities Soc 52. https://doi.org/10.1016/j.scs.2019.101842
Akın M, Akın G (2007) Suyun Önemi, Türkiye’de Su Potansiyeli, Su Havzaları ve Su Kirliliği. In: Ankara Üniversitesi Dil ve Tarih-Coğrafya Fakültesi Dergisi (Vol. 47). https://dspace.ankara.edu.tr/xmlui/handle/20.500.12575/65988
Alimardani M, Hashemkhani Zolfani S, Aghdaie MH, Tamošaitiene J (2013) A novel hybrid SWARA and VIKOR methodology for supplier selection in an agile environment. Technol Econ Dev Econ 19(3):533–548. https://doi.org/10.3846/20294913.2013.814606
Álvarez-Rodríguez C, Martín-Gamboa M, Iribarren D (2020) Sustainability-oriented efficiency of retail supply chains: a combination of life cycle assessment and dynamic network data envelopment analysis. Sci Total Environ 705. https://doi.org/10.1016/j.scitotenv.2019.135977
An Q, Meng F, Xiong B, Wang Z, Chen X (2020) Assessing the relative efficiency of Chinese high-tech industries: a dynamic network data envelopment analysis approach. Ann Oper Res 290(1–2):707–729. https://doi.org/10.1007/s10479-018-2883-2
Ang MS, Duyag J, Tee KC, Sy CL (2019) A multi-period and multi-criterion optimization model integrating multiple input configurations, reuse, and disposal options for a wastewater treatment facility. J Clean Prod 231:1437–1449. https://doi.org/10.1016/j.jclepro.2019.05.246
Ayyildiz E, Taskin Gumus A, Erkan M (2020) Individual credit ranking by an integrated interval type-2 trapezoidal fuzzy Electre methodology. Soft Comput. https://doi.org/10.1007/s00500-020-04929-1
Ayyıldız E, Demirci E (2018) Determining the quality of life the cities in Turkey Using SWARA integrated TOPSIS method. Pamukkale Univ J Soc Sci Inst. https://doi.org/10.5505/pausbed.2018.16870
Baghery M, Yousefi S, Rezaee MJ (2018) Risk measurement and prioritization of auto parts manufacturing processes based on process failure analysis, interval data envelopment analysis and grey relational analysis. J Intell Manuf 29(8):1803–1825. https://doi.org/10.1007/s10845-016-1214-1
Balali A, Hakimelahi A, Valipour A (2020) Identification and prioritization of passive energy consumption optimization measures in the building industry: an Iranian case study. J Build Eng 30. https://doi.org/10.1016/j.jobe.2020.101239
Balkema AJ, Preisig HA, Otterpohl R, Lambert FJD (2002) Indicators for the sustainability assessment of wastewater treatment systems. Urban Water 4(2):153–161. https://doi.org/10.1016/S1462-0758(02)00014-6
Balki MK, Erdoğan S, Aydın S, Sayin C (2020) The optimization of engine operating parameters via SWARA and ARAS hybrid method in a small SI engine using alternative fuels. J Clean Prod, 258. https://doi.org/10.1016/j.jclepro.2020.120685
Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale ineeffciencies in data envelopment analysis. Manag Sci 30(9):1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
Baušys R, Juodagalvienė B, Žiūrienė R, Pankrašovaitė I, Kamarauskas J, Usovaitė A, Gaižauskas D (2020) The residence plot selection model for family house in Vilnius by neutrosophic waspas method. Int J Strategic Prop Manag 24(3):182–196. https://doi.org/10.3846/ijspm.2020.12107
Bottero M, Comino E, Riggio V (2011) Application of the analytic hierarchy process and the analytic network process for the assessment of different wastewater treatment systems. Environ Model Softw 26(10):1211–1224. https://doi.org/10.1016/j.envsoft.2011.04.002
Castellet L, Molinos-Senante M (2016) Efficiency assessment of wastewater treatment plants: A data envelopment analysis approach integrating technical, economic, and environmental issues. J Environ Manag 167:160–166. https://doi.org/10.1016/j.jenvman.2015.11.037
Chaabouni S (2019) China’s regional tourism efficiency: a two-stage double bootstrap data envelopment analysis. J Destination Mark Manag 11:183–191. https://doi.org/10.1016/j.jdmm.2017.09.002
Chandra P, Cooper WW, Li S, Rahman A (1998) Using DEA to evaluate 29 Canadian textile companies - Considering returns to scale. Int J Prod Econ 54(2):129–141. https://doi.org/10.1016/S0925-5273(97)00135-7
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Operational Res 2(6):429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chen W, Panahi M, Tsangaratos P, Shahabi H, Ilia I, Panahi S, Li S, Jaafari A, Ahmad BBin (2019) Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. Catena 172:212–231. https://doi.org/10.1016/j.catena.2018.08.025
Dahooie J, Zavadskas E, Abolhasani M, Vanaki A, Turskis Z (2018) A novel approach for evaluation of projects using an interval–valued fuzzy additive ratio assessment (ARAS) method: a case study of oil and gas well drilling projects. Symmetry 10(2):45. https://doi.org/10.3390/sym10020045
Debreu G (1951) The coefficient of resource utilization. Econometrica 19(3):273. https://doi.org/10.2307/1906814
Dinc M, Haynes KE (1999) Sources of regional inefficiency An integrated shift-share, data envelopment analysis and input-output approach. Ann Reg Sci 33(4):469–489. https://doi.org/10.1007/s001680050116
Dobos I, Vörösmarty G (2019) Inventory-related costs in green supplier selection problems with Data Envelopment Analysis (DEA). Int J Prod Econ 209:374–380. https://doi.org/10.1016/j.ijpe.2018.03.022
Erdoğan S, Balki MK, Aydın S, Sayin C (2019) The best fuel selection with hybrid multiple-criteria decision making approaches in a CI engine fueled with their blends and pure biodiesels produced from different sources. Renew Energy 134:653–668. https://doi.org/10.1016/j.renene.2018.11.060
Farrell MJ (1957) The measurement of productive efficiency. J R Stat Soc Ser A 120(3):253. https://doi.org/10.2307/2343100
Gaulke LS, Weiyang X, Scanlon A, Henck A, Hinckley T (2010) Evaluation criteria for implementation of a sustainable sanitation and wastewater treatment system at Jiuzhaigou National Park, Sichuan Province, China. Environ Manag 45(1):93–104. https://doi.org/10.1007/s00267-009-9398-1
Geng Q, Ren Q, Nolan RH, Wu P, Yu Q (2019) Assessing China’s agricultural water use efficiency in a green-blue water perspective: A study based on data envelopment analysis. Ecol Indic 96:329–335. https://doi.org/10.1016/j.ecolind.2018.09.011
Ghaderi S, Javid A, Ghaffarzadeh H, Lotf FH (2019) Optimal selection of wastewater treatment and location for subway stations using mathematical techniques: Five stations at the eastern end of Tehran subway line 2. Kuwait J Sci 46(1):99–106
Ghenai C, Albawab M, Bettayeb M (2020) Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended SWARA/ARAS hybrid method. Renew Energy 146:580–597. https://doi.org/10.1016/j.renene.2019.06.157
Gomes LA, Santos AF, Pinheiro CT, Góis JC, Quina MJ (2020) Screening of waste materials as adjuvants for drying sewage sludge based on environmental, technical and economic criteria. J Clean Prod 259:120927. https://doi.org/10.1016/j.jclepro.2020.120927
Guajardo SA (2020) Nonprofit public libraries and technical efficiency: An application of data envelopment analysis to technology-based outputs. Libr Inf Sci Res 42(1). https://doi.org/10.1016/j.lisr.2019.100995
Hashemkhani Zolfani S, Bahrami M (2014) Investment prioritizing in high tech industries based on SWARA-COPRAS approach. Technol Econ Dev Econ 20(3):534–553. https://doi.org/10.3846/20294913.2014.881435
Hashemkhani Zolfani S, Salimi J, Maknoon R, Simona K (2015) Technology foresight about R&D projects selection; application of SWARA method at the policy making level. Eng Econ 26(5):571–580. https://doi.org/10.5755/j01.ee.26.5.9571
Hashemkhani Zolfani S, Yazdani M, Kazimieras Zavadskas E (2018) An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process. Soft Comput 22:7399–7405. https://doi.org/10.1007/s00500-018-3092-2
Hermoso-Orzáez MJ, García-Alguacil M, Terrados-Cepeda J, Brito P (2020) Measurement of environmental efficiency in the countries of the European Union with the enhanced data envelopment analysis method (DEA) during the period 2005–2012. Environ Sci Pollut Res 27(13):15691–15715. https://doi.org/10.1007/s11356-020-08029-3
Hernández-Sancho F, Molinos-Senante M, Sala-Garrido R (2011) Energy efficiency in Spanish wastewater treatment plants: a non-radial DEA approach. Sci Total Environ 409(14):2693–2699. https://doi.org/10.1016/j.scitotenv.2011.04.018
Hernández-Sancho Francesc, Sala-Garrido R (2009) Technical efficiency and cost analysis in wastewater treatment processes: a DEA approach. Desalination 249(1):230–234. https://doi.org/10.1016/j.desal.2009.01.029
Homburg C (2001) Using data envelopment analysis to benchmark activities. Int J Prod Econ 73(1):51–58. https://doi.org/10.1016/S0925-5273(01)00194-3
Hu W, Guo Y, Tian J, Chen L (2019) Eco-efficiency of centralized wastewater treatment plants in industrial parks: a slack-based data envelopment analysis. Resour Conserv Recycling 141:176–186. https://doi.org/10.1016/j.resconrec.2018.10.020
Ighravwe DE, Oke SA (2019) A multi-criteria decision-making framework for selecting a suitable maintenance strategy for public buildings using sustainability criteria. J Build Eng 24. https://doi.org/10.1016/j.jobe.2019.100753
Ijadi Maghsoodi A, Ijadi Maghsoodi A, Poursoltan P, Antucheviciene J, Turskis Z (2019) Dam construction material selection by implementing the integrated SWARA–CODAS approach with target-based attributes. Arch Civ Mech Eng 19(4):1194–1210. https://doi.org/10.1016/j.acme.2019.06.010
Ilangkumaran M, Sakthivel G, Sasirekha V (2014) Waste water treatment technology selection using FAHP and GRA approaches. Int J Environ Waste Manag 14(4):392–413. https://doi.org/10.1504/IJEWM.2014.066594
Imamoğlu G, Köse Y, Demirci E (2017) Sağlik kuruluşlarinin su kullanim etkinliklerinin iller düzeyinde belirlenmesi. Turk Hij ve Deney Biyol Derg 74:63–72. https://doi.org/10.5505/TurkHijyen.2017.92259
Jaafari A, Zenner EK, Panahi M, Shahabi H (2019) Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agric For Meteorol 266–267:198–207. https://doi.org/10.1016/j.agrformet.2018.12.015
Jiang H, Hua M, Zhang J, Cheng P, Ye Z, Huang M, Jin Q (2020) Sustainability efficiency assessment of wastewater treatment plants in China: a data envelopment analysis based on cluster benchmarking. J Clean Prod 244. https://doi.org/10.1016/j.jclepro.2019.118729
Kao C, Liu ST (2016) A parallel production frontiers approach for intertemporal efficiency analysis: the case of Taiwanese commercial banks. Eur J Operational Res 255(2):411–421. https://doi.org/10.1016/j.ejor.2016.04.047
Karabasevic D, Zavadskas EK, Turskis Z, Stanujkic D (2016) The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties. Informatica 27(1):49–65. https://doi.org/10.15388/Informatica.2016.76
Karagiannidis A, Perkoulidis G (2009) A multi-criteria ranking of different technologies for the anaerobic digestion for energy recovery of the organic fraction of municipal solid wastes. Bioresour Technol 100(8):2355–2360. https://doi.org/10.1016/j.biortech.2008.11.033
Karlsson CSJ, Kalantari Z, Mörtberg U, Olofsson B, Lyon SW (2017) Natural hazard susceptibility assessment for road planning using spatial multi-criteria analysis. Environ Manag 60(5):823–851. https://doi.org/10.1007/s00267-017-0912-6
Keršuliene V, Turskis Z (2011) Integrated fuzzy multiple criteria decision making model for architect selection. Technol Economic Dev Econ 17(4):645–666. https://doi.org/10.3846/20294913.2011.635718
Keršulienė V, Zavadskas EK, Turskis Z (2010) Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). J Bus Econ Manag 11(2):243–258.
Kose Y, Demirci E (2020) Improving quality of product and process in the manufacturing of particleboard with an integrated quality function deployment approach. In: Studies in systems, decision and control, vol. 279. Springer, pp 35–55. https://doi.org/10.1007/978-3-030-42188-5_3
Lee H, Kim C (2014) Benchmarking of service quality with data envelopment analysis. Expert Syst Appl 41(8):3761–3768. https://doi.org/10.1016/j.eswa.2013.12.008
Li D, Wang MQ, Lee C (2020) The waste treatment and recycling efficiency of industrial waste processing based on two-stage data envelopment analysis DEA with undesirable inputs. J Clean Prod 242. https://doi.org/10.1016/j.jclepro.2019.118279
Li F, Zhu Q, Liang L (2019) A new data envelopment analysis based approach for fixed cost allocation. Ann Oper Res 274(1–2):347–372. https://doi.org/10.1007/s10479-018-2819-x
Li HX, Li Y, Jiang B, Zhang L, Wu X, Lin J (2020) Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis. Renew Energy 149:1414–1423. https://doi.org/10.1016/j.renene.2019.10.143
Liang W, Zhao G, Hong C (2019) Selecting the optimal mining method with extended multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) approach. Neural Comput Appl 31(10):5871–5886. https://doi.org/10.1007/s00521-018-3405-5
Liu X, Guo P, Guo S (2019) Assessing the eco-efficiency of a circular economy system in China’s coal mining areas: Emergy and data envelopment analysis. J Clean Prod 206:1101–1109. https://doi.org/10.1016/j.jclepro.2018.09.218
Longo S, d’Antoni BM, Bongards M, Chaparro A, Cronrath A, Fatone F, Lema JM, Mauricio-Iglesias M, Soares A, Hospido A (2016) Monitoring and diagnosis of energy consumption in wastewater treatment plants. A state of the art and proposals for improvement. In: Applied energy, vol. 179. Elsevier Ltd, pp 1251–1268. https://doi.org/10.1016/j.apenergy.2016.07.043
Lorenzo-Toja Y, Vázquez-Rowe I, Chenel S, Marín-Navarro D, Moreira MT, Feijoo G (2015) Eco-efficiency analysis of Spanish WWTPs using the LCA+DEA method. Water Res 68:651–666. https://doi.org/10.1016/j.watres.2014.10.040
Luo Q, Miao C, Sun L, Meng X, Duan M (2019) Efficiency evaluation of green technology innovation of China’s strategic emerging industries: an empirical analysis based on Malmquist-data envelopment analysis index. J Clean Prod 238. https://doi.org/10.1016/j.jclepro.2019.117782
Mauricio-Iglesias M, Longo S, Hospido A (2020) Designing a robust index for WWTP energy efficiency: the ENERWATER water treatment energy index. Sci Total Environ 713. https://doi.org/10.1016/j.scitotenv.2020.136642
Mishra AR, Rani P, Pandey K, Mardani A, Streimikis J, Streimikiene D, Alrasheedi M (2020) Novel multi-criteria intuitionistic fuzzy SWARA-COPRAS approach for sustainability evaluation of the bioenergy production process. Sustainability 12(10). https://doi.org/10.3390/su12104155
Özcan E (2014) Wastewater management in Turkey. https://webdosya.csb.gov.tr/db/tay/webmenu/webmenu13378.pdf
Özden ÜH (2008) Veri zarflama analizi (VZA) ile Türkiye’deki vakıf üniversitelerinin etkinliğinin ölçülmesi İstanbul Üniversitesi İşletme Fakültesi Derg Istanb Univ J Sch Bus Adm Cilt 37(2):167–185. www.ifdergisi.org
Pan D, Hong W, Kong F (2020) Efficiency evaluation of urban wastewater treatment: evidence from 113 cities in the Yangtze River Economic Belt of China. J Environ Manag 270. https://doi.org/10.1016/j.jenvman.2020.110940
Pourghasemi HR, Gayen A, Panahi M, Rezaie F, Blaschke T (2019) Multi-hazard probability assessment and mapping in Iran. Sci Total Environ 692:556–571. https://doi.org/10.1016/j.scitotenv.2019.07.203
Rani P, Mishra AR, Mardani A, Cavallaro F, Štreimikiene D, Khan SAR (2020) Pythagorean fuzzy SWARA-VIKOR framework for performance evaluation of solar panel selection. Sustainability 12(10). https://doi.org/10.3390/su12104278
Rashidi K, Cullinane K (2019) Evaluating the sustainability of national logistics performance using Data Envelopment Analysis. Transp Policy 74:35–46. https://doi.org/10.1016/j.tranpol.2018.11.014
Rouyendegh BD, Yildizbasi A, Yilmaz, I (2020) Evaluation of retail ındustry performance ability through ıntegrated ıntuitionistic fuzzy TOPSIS and data envelopment analysis approach. Soft Comput. https://doi.org/10.1007/s00500-020-04669-2
Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15(3):234–281. https://doi.org/10.1016/0022-2496(77)90033-5
Sala-Garrido R, Molinos-Senante M, Hernández-Sancho F (2011) Comparing the efficiency of wastewater treatment technologies through a DEA metafrontier model. Chem Eng J 173(3):766–772. https://doi.org/10.1016/j.cej.2011.08.047
Shabanpour H, Yousefi S, Saen RF (2017) Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks. J Clean Prod 142:1098–1107. https://doi.org/10.1016/j.jclepro.2016.08.147
Shetty A, Basri S (2020) Assessing the technical efficiency of traditional and corporate agents in indian life insurance industry: slack-based data envelopment analysis approach. Glob Bus Rev 21(2):490–506. https://doi.org/10.1177/0972150917749722
Singh RK, Modgil S (2020) Supplier selection using SWARA and WASPAS – a case study of Indian cement industry. Meas Bus Excell 24(2):243–265. https://doi.org/10.1108/MBE-07-2018-0041
Stanujkic D, Karabasevic D, Zavadskas EK (2015) A framework for the selection of a packaging design based on the SWARA method. Eng Econ 26(2):181–187. https://doi.org/10.5755/j01.ee.26.2.8820
Sueyoshi T, Goto M (2012) Data envelopment analysis for environmental assessment: comparison between public and private ownership in petroleum industry. Eur J Operational Res 216(3):668–678. https://doi.org/10.1016/j.ejor.2011.07.046
Supciller AA, Toprak F (2020) Selection of wind turbines with multi-criteria decision making techniques involving neutrosophic numbers: a case from Turkey. Energy 207. https://doi.org/10.1016/j.energy.2020.118237
Tashayo B, Honarbakhsh A, Azma A, Akbari M (2020) Combined fuzzy AHP–GIS for agricultural land suitability modeling for a watershed in Southern Iran. Environ Manag 1–13. https://doi.org/10.1007/s00267-020-01310-8
Tavana M, Kaviani MA, Di Caprio D, Rahpeyma B (2016) A two-stage data envelopment analysis model for measuring performance in three-level supply chains. Measurement 78:322–333. https://doi.org/10.1016/j.measurement.2015.10.023
Turkish Statistical Institute (2018) Environ Indic. https://cevreselgostergeler.csb.gov.tr/kanalizasyon-sebekesiyle-hizmet-verilen-nufus-i-85747
Vizzari M, Modica G (2013) Environmental effectiveness of swine sewage management: A multicriteria ahp-based model for a reliable quick assessment. Environ Manag 52(4):1023–1039. https://doi.org/10.1007/s00267-013-0149-y
Yang W, Li L (2018) Efficiency evaluation of industrial waste gas control in China: a study based on data envelopment analysis (DEA) model. J Clean Prod 179:1–11. https://doi.org/10.1016/j.jclepro.2017.12.277
Yazdani M, Hashemkhani Zolfani S, Zavadskas EK (2016) New integration of MCDM methods and QFD in the selection of green suppliers. J Bus Econ Manag 17(6):1097–1113. https://doi.org/10.3846/16111699.2016.1165282
Yildiz A, Ayyildiz E, Gumus AT, Ozkan C (2020) A modified balanced scorecard based hybrid pythagorean fuzzy AHP-topsis methodology for ATM site selection problem. Int J Inf Technol Decis Mak 19(02):365–384. https://doi.org/10.1142/S0219622020500017
Yücenur GN, Çaylak Ş, Gönül G, Postalcıoğlu M (2020) An integrated solution with SWARA&COPRAS methods in renewable energy production: city selection for biogas facility. Renew Energy 145:2587–2597. https://doi.org/10.1016/j.renene.2019.08.011
Zavadskas EK, Ustinovichius L, Turskis Z, Shevchenko G (2007) Application of verbal methods to multi – attribute comparative analysis of investments risk alternatives in construction. Computer Model New Technol 12(4):30–37
Zavadskas EK, Bausys R, Juodagalviene B, Garnyte-Sapranaviciene I (2017) Model for residential house element and material selection by neutrosophic MULTIMOORA method. Eng Appl Artif Intell 64:315–324. https://doi.org/10.1016/j.engappai.2017.06.020
Zavadskas EK, Čereška A, Matijošius J, Rimkus A, Bausys R (2019) Internal combustion engine analysis of energy ecological parameters by neutrosophic multimoora and SWARA methods. Energies 12(8). https://doi.org/10.3390/en12081415
Zhao H, Guo S, Zhao H (2019) Provincial energy efficiency of China quantified by three-stage data envelopment analysis. Energy 166:96–107. https://doi.org/10.1016/j.energy.2018.10.063
Zolfani SH, Chatterjee P (2019) Comparative evaluation of sustainable design based on Step-Wise Weight Assessment Ratio Analysis (SWARA) and Best Worst Method (BWM) methods: a perspective on household furnishing materials. Symmetry 11(1). https://doi.org/10.3390/sym11010074
Zolfani SH, Esfahani MH, Bitarafan M, Zavadskas EK, Arefi SL (2013) Developing a new hybrid MCDM method for selection of the optimal alternative of mechanical longitudinal ventilation of tunnel pollutants during automobile accidents. Transport 28(1):89–96. https://doi.org/10.3846/16484142.2013.782567
Zolfani SH, Zavadskas EK, Turskis Z (2013) Design of products with both international and local perspectives based on Yin-Yang balance theory and Swara method. Economic Res 26(2):153–166. https://doi.org/10.1080/1331677x.2013.11517613
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Ayyildiz, E., Yildiz, A., Taskin Gumus, A. et al. An Integrated Methodology Using Extended Swara and Dea for the Performance Analysis of Wastewater Treatment Plants: Turkey Case. Environmental Management 67, 449–467 (2021). https://doi.org/10.1007/s00267-020-01381-7
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DOI: https://doi.org/10.1007/s00267-020-01381-7