Abstract
In this investigation, we have put forward a novel multi-criteria decision-making (MCDM) technique, intuitionistic fuzzy best–worst method (intuitionistic fuzzy BWM). Moreover, we have designed a novel hybrid MCDM technique called intuitionistic fuzzy best–worst analytic hierarchy process (intuitionistic fuzzy BWAHP) which is the amalgamation of intuitionistic fuzzy BWM and analytic hierarchy process (AHP). In this study, BWM finds its use to evaluate the weightage (or priority value) of criteria, and using AHP, the local weights of alternatives are decided. The proposed technique is utilized to recognize the most significant alternative (or indicator), for the efficiency of a water treatment plant. Observing the result, it can be said that the ‘water quality’ is the most responsible alternative. The consistency ratio value which is found by our proposed method is less, compared to the existing BWM and fuzzy BWM techniques. Finally, using a comparative study and sensitivity analysis, we verify the findings produced by our proposed method.
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Abbreviations
- A 1 :
-
Length/density of pipelines
- A 2 :
-
Weather pattern
- A 3 :
-
Quality of incoming water
- A 4 :
-
Labour efficiency
- A 5 :
-
Availability of dosing chemicals
- A 6 :
-
Efficiency of instrument
- AI:
-
Absolute importance
- AHP:
-
Analytic hierarchy process
- B :
-
Beneficial
- BWM:
-
Best–worst method
- C 1 :
-
Operational efficiency
- C 2 :
-
Operational expenditure
- C 3 :
-
Impact of climate change
- CI:
-
Consistency index
- CR:
-
Consistency ratio
- DM:
-
Decision maker
- EI:
-
Equal importance
- Fuzzy AHP:
-
Fuzzy analytic hierarchy process
- Fuzzy ANP:
-
Fuzzy analytic network process
- Fuzzy DEA:
-
Fuzzy data envelopment analysis
- Fuzzy ELECTRE:
-
Fuzzy elimination and choice translating reality
- Fuzzy TOPSIS:
-
Fuzzy technique for order preference by similarities to ideal solution
- Fuzzy VIKOR:
-
Fuzzy VIšekriterijumsko KOmpromisno Rangiranje
- IFRC:
-
Intuitionistic fuzzy reference comparison
- IFS:
-
Intuitionistic fuzzy sets
- Intuitionistic fuzzy BWM:
-
Intuitionistic fuzzy best–worst method
- Intuitionistic fuzzy BWAHP:
-
Intuitionistic fuzzy best–worst analytic hierarchy process
- MCDM:
-
Multi-criteria decision making
- MI:
-
Moderate importance
- NB:
-
Non-beneficial
- PV:
-
Priority value
- PVs:
-
Priority values
- SI:
-
Strong importance
- S.I.:
-
Score index
- TIFN:
-
Triangular intuitionistic fuzzy number
- VSI:
-
Very strong importance
- WTP:
-
Water treatment plant
References
Abdullah L (2013) Fuzzy multi criteria decision making and its applications: a brief review of category. Proc Social Behav Sci 97:131–136
Ahmed SS, Dey N, Ashour AS, Sifaki-Pistolla D, Bălas-Timar D, Balas VE, Tavares JMR (2017) Effect of fuzzy partitioning in Crohn’s disease classification: a neuro-fuzzy-based approach. Med Biol Eng Compu 55(1):101–115
Ahn BS (2017) The analytic hierarchy process with interval preference statements. Omega 67:177–185
Asadabadi MR, Chang E, Saberi M (2019) Are MCDM methods useful? A critical review of analytic hierarchy process (AHP) and analytic network process (ANP). Cogent Eng 6(1):1623153
Atanassov KT (1999) Intuitionistic fuzzy sets. In Intuitionistic fuzzy sets (pp. 1–137). Physica, Heidelberg
Azar AT, El-Said SA, Balas VE, Olariu T (2013) Linguistic hedges fuzzy feature selection for differential diagnosis of Erythemato-Squamous diseases. In: Balas VE, Fodor J, Várkonyi-Kóczy AR, Dombi J, Jain LC (eds) Soft computing applications. Springer, Berlin, Heidelberg, pp 487–500
Baidya D, Roy RG (2018) Speed control of DC motor using fuzzy-based intelligent model reference adaptive control scheme. In: Bera R, Sarkar SK, Chakraborty S (eds) Advances in communication, devices and networking. Springer, Singapore, pp 729–735
Balas MM, Balas VE (2008) World knowledge for control applications by fuzzy-interpolative systems. Int J Comput Commun Control 3:28–32
Belanche L, Sànchez M, Cortés U, Serra P (1992) A knowledge-based system for the diagnosis of waste-water treatment plants. In: International conference on industrial, engineering 2018 and other applications of applied intelligent systems (pp. 324–336). Springer, Berlin, Heidelberg
Carlsson C, Fuller R (1996) Fuzzy multiple criteria decision making: Recent developments. Fuzzy Sets Syst 78:139–153
Cath TY, Hancock NT, Lundin CD, Hoppe-Jones C, Drewes JE (2010) A multi-barrier osmotic dilution process for simultaneous desalination and purification of impaired water. J Membr Sci 362(1–2):417–426
Chai J, Liu JN, Ngai EW (2013) Application of decision-making techniques in supplier selection: a systematic review of literature. Exp Syst Appl 40(10):3872–3885
Chang DY (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95(3):649–655
Chaturvedi S, Dave PN (2012) Removal of iron for safe drinking water. Desalination 303:1–11
Chiao KP (2016) The multi-criteria group decision making methodology using type 2 fuzzy linguistic judgments. Appl Soft Comput 49:189–211
Choudhury S, Saha AK (2018) Prediction of operation efficiency of water treatment plant with the help of multi-criteria decision-making. Water Conserv Sci Eng 3(2):79–90
Choudhury S, Saha AK, Majumder M (2020) Optimal location selection for installation of surface water treatment plant by Gini coefficient-based analytical hierarchy process. Environ Dev Sustain 22(5):4073–4099
Deepak S (2014) Treatment of dairy waste water by electro coagulation using aluminum electrodes and settling, filtration studies. Int J ChemTech Res 6(1):591–599
Deshmukh A, Boo C, Karanikola V, Lin S, Straub AP, Tong T, Elimelech M (2018) Membrane distillation at the water-energy nexus: limits, opportunities, and challenges. Energy Environ Sci 11(5):1177–1196
Eiche E, Hochschild M, Haryono E, Neumann T (2016) Characterization of recharge and flow behaviour of different water sources in Gunung Kidul and its impact on water quality based on hydrochemical and physico-chemical monitoring. Appl Water Sci 6(3):293–307
Fogel D, Isaac-Renton J, Guasparini R, Moorehead W, Ongerth J (1993) Removing giardia and cryptosporidium by slow sand filtration. J Am Water Works Ass 85(11):77–84
French S (1984) Fuzzy decision analysis: some criticisms. In: Zimmermann HJ (ed) TIMS/studies in the management sciences, vol 20. Elsevier Science Publishers, Amsterdam, pp 29–44
Gaines BR (1976) Foundations of fuzzy reasoning. Int J Man Mach Stud 8(6):623–668
Ghoushchi SJ, Yousefi S, Khazaeili M (2019) An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures. Appl Soft Comput 81:105505
Guimarães AMC, Leal JE, Mendes P (2018) Discrete-event simulation software selection for manufacturing based on the maturity model. Comput Ind 103:14–27
Gündoğdu FK, Kahraman C (2020) A novel spherical fuzzy analytic hierarchy process and its renewable energy application. Soft Comput 24(6):4607–4621
Guo P, Tanaka H (2001) Fuzzy DEA: a perceptual evaluation method. Fuzzy Sets Syst 119(1):149–160
Guo S, Zhao H (2017) Fuzzy best-worst multi-criteria decision-making method and its applications. Knowl-Based Syst 121:23–31
Gupta A, Singh KAWALJEET, Verma R (2010) A critical study and comparison of manufacturing simulation softwares using analytic hierarchy process. J Eng Sci Technol 5(1):108–129
Hou Y, Chu W, Ma M (2012) Carbonaceous and nitrogenous disinfection by-product formation in the surface and ground water treatment plants using Yellow River as water source. J Environ Sci 24(7):1204–1209
Jadhav AS, Sonar RM (2011) Framework for evaluation and selection of the software packages: a hybrid knowledge based system approach. J Syst Softw 84(8):1394–1407
Jin F, Ni Z, Chen H, Li Y (2016) Approaches to group decision making with intuitionistic fuzzy preference relations based on multiplicative consistency. Knowl-Based Syst 97:48–59
Kaindl N, Tillman U, Möbius CH (1999) Enhancement of capacity and efficiency of a biological waste water treatment plant. Water Sci Technol 40(11–12):231–239
Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24(1):65–75
Maghsoodi AI, Mosavat M, Hafezalkotob A, Hafezalkotob A (2019) Hybrid hierarchical fuzzy group decision-making based on information axioms and BWM: Prototype design selection. Comput Ind Eng 127:788–804
Majumder P, Majumder M, Saha AK, Sarkar K, Nath S (2019) Real time reliability monitoring of hydro-power plant by combined cognitive decision-making technique. Int J Energy Res 43(9):4912–4939
Mendes P Jr, Leal JE, Thomé AMT (2016) A maturity model for demand-driven supply chains in the consumer product goods industry. Int J Prod Econ 179:153–165
Mohanty RP, Agarwal R, Choudhury AK, Tiwari MK (2005) A fuzzy ANP-based approach to R&D project selection: a case study. Int J Prod Res 43(24):5199–5216
Murat YS (2006) Comparison of fuzzy logic and artificial neural networks approaches in vehicle delay modeling. Transp Res Part C Emerg Technol 14(5):316–334
Murat YS, Gedizlioglu E (2005) A fuzzy logic multi-phased signal control model for isolated junctions. Transp Res Part C Emerg Technol 13(1):19–36
Murat YS (2017) Modeling route choice behavior in transportation networks using fuzzy information axiom approach. In: Online proceedings of the 96th annual meeting of the transportation research board (TRB), Washington D.C., USA
Murat YŞ, Kikuchi S (2007) The fuzzy optimization approach: a comparison with the classical optimization approach using the problem of timing a traffic signal. Transp Res Record 2024:82–91
Murat YŞ, Uludag N (2008) Route choice modelling in urban transportation networks using fuzzy logic and logistic regression methods. J Sci Ind Res 67:19–27
Murat YS, Arslan T, Cakici Z, Akçam C (2016) Analytical hierarchy process (AHP) based decision support system for urban intersections in transportation planning. In: Ocalir-Akunal FV (ed) Using decision support systems for transportation planning efficiency. IGI Global, pp 203–222
Otay İ, Oztaysi B, Onar SC, Kahraman C (2017) Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowl-Based Syst 133:90–106
Pappis CP, Mamdani EH (1977) A fuzzy logic controller for a trafc junction. IEEE Trans Syst Man Cybern 7(10):707–717
Plous S (1993) The psychology of judgment and decision making. McGraw-Hill Book Company
Pohekar SD, Ramachandran M (2004) Application of multi-criteria decision making to sustainable energy planning: a review. Renew Sustain Energy Rev 8(4):365–381
Rajesh G, Malliga P (2013) Supplier selection based on AHP QFD methodology. Proc Eng 64:1283–1292
Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53:49–57
Richardson SD, Postigo C (2011) Drinking water disinfection by-products. In: Barcelo D (ed) Emerging organic contaminants and human health. Springer, Berlin, Heidelberg, pp 93–137
Saaty TL (1980) The analytical hierarchy process, planning, priority. Resource allocation. RWS publications, USA
Saha AK, Choudhury S, Majumder M (2017) Performance efficiency analysis of water treatment plants by using MCDM and neural network model. MATTER Int J Sci Technol 3(1):27
Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15(3):234–281
Satty TL (1980) The analytic hierarchy process. Analytic Hierarchy Process
Sevkli M (2010) An application of the fuzzy ELECTRE method for supplier selection. Int J Prod Res 48(12):3393–3405
Shemshadi A, Shirazi H, Toreihi M, Tarokh MJ (2011) A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Exp Syst Appl 38(10):12160–12167
Szmidt E, Kacprzyk J (2000) Distances between intuitionistic fuzzy sets. Fuzzy Sets Syst 114(3):505–518
Szmidt E, Kacprzyk J (2001) Entropy for intuitionistic fuzzy sets. Fuzzy Sets Syst 118(3):467–477
Tam MC, Tummala VR (2001) An application of the AHP in vendor selection of a telecommunications system. Omega 29(2):171–182
Tandukar S, Sherchand JB, Bhandari D, Sherchan SP, Malla B, Ghaju Shrestha R, Haramoto E (2018) Presence of human enteric viruses, protozoa, and indicators of pathogens in the Bagmati River, Nepal. Pathogens 7(2):38
Teodorovic D, Kikuchi S (1990) Transportation route choice model using fuzzy inference technique. In: [1990] Proceedings. First international symposium on uncertainty modeling and analysis (pp. 140–145). IEEE
Tian ZP, Wang JQ, Zhang HY (2018) An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods. Appl Soft Comput 72:636–646
Wan SP, Wang F, Lin LL, Dong JY (2016) Some new generalized aggregation operators for triangular intuitionistic fuzzy numbers and application to multi-attribute group decision making. Comput Ind Eng 93:286–301
Wu Z, Wang X, Chen Y, Cai Y, Deng J (2018) Assessing river water quality using water quality index in Lake Taihu Basin, China. Sci Total Environ 612:914–922
Xu Z, Liao H (2013) Intuitionistic fuzzy analytic hierarchy process. IEEE Trans Fuzzy Syst 22(4):749–761
Yong D (2006) Plant location selection based on fuzzy TOPSIS. Int J Adv Manuf Technol 28(7–8):839–844
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
Zhao H, Guo S (2015) External benefit evaluation of renewable energy power in China for sustainability. Sustainability 7(5):4783–4805
Zheng G, Zhu N, Tian Z, Chen Y, Sun B (2012) Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments. Saf Sci 50(2):228–239
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Majumder, P., Baidya, D. & Majumder, M. Application of novel intuitionistic fuzzy BWAHP process for analysing the efficiency of water treatment plant. Neural Comput & Applic 33, 17389–17405 (2021). https://doi.org/10.1007/s00521-021-06326-7
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DOI: https://doi.org/10.1007/s00521-021-06326-7