Skip to main content
Log in

Application of novel intuitionistic fuzzy BWAHP process for analysing the efficiency of water treatment plant

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

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

  1. Abdullah L (2013) Fuzzy multi criteria decision making and its applications: a brief review of category. Proc Social Behav Sci 97:131–136

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. Ahn BS (2017) The analytic hierarchy process with interval preference statements. Omega 67:177–185

    Article  Google Scholar 

  4. 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

    Article  Google Scholar 

  5. Atanassov KT (1999) Intuitionistic fuzzy sets. In Intuitionistic fuzzy sets (pp. 1–137). Physica, Heidelberg

  6. 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

    Chapter  Google Scholar 

  7. 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

    Chapter  Google Scholar 

  8. Balas MM, Balas VE (2008) World knowledge for control applications by fuzzy-interpolative systems. Int J Comput Commun Control 3:28–32

    Google Scholar 

  9. 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

  10. Carlsson C, Fuller R (1996) Fuzzy multiple criteria decision making: Recent developments. Fuzzy Sets Syst 78:139–153

    Article  MathSciNet  MATH  Google Scholar 

  11. 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

    Article  Google Scholar 

  12. 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

    Article  Google Scholar 

  13. Chang DY (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95(3):649–655

    Article  MATH  Google Scholar 

  14. Chaturvedi S, Dave PN (2012) Removal of iron for safe drinking water. Desalination 303:1–11

    Article  Google Scholar 

  15. Chiao KP (2016) The multi-criteria group decision making methodology using type 2 fuzzy linguistic judgments. Appl Soft Comput 49:189–211

    Article  Google Scholar 

  16. 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

    Article  Google Scholar 

  17. 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

    Article  Google Scholar 

  18. 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

    Google Scholar 

  19. 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

    Article  Google Scholar 

  20. 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

    Article  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. 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

    Google Scholar 

  23. Gaines BR (1976) Foundations of fuzzy reasoning. Int J Man Mach Stud 8(6):623–668

    Article  MathSciNet  MATH  Google Scholar 

  24. 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

    Article  Google Scholar 

  25. Guimarães AMC, Leal JE, Mendes P (2018) Discrete-event simulation software selection for manufacturing based on the maturity model. Comput Ind 103:14–27

    Article  Google Scholar 

  26. Gündoğdu FK, Kahraman C (2020) A novel spherical fuzzy analytic hierarchy process and its renewable energy application. Soft Comput 24(6):4607–4621

    Article  Google Scholar 

  27. Guo P, Tanaka H (2001) Fuzzy DEA: a perceptual evaluation method. Fuzzy Sets Syst 119(1):149–160

    Article  MathSciNet  Google Scholar 

  28. Guo S, Zhao H (2017) Fuzzy best-worst multi-criteria decision-making method and its applications. Knowl-Based Syst 121:23–31

    Article  Google Scholar 

  29. 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

    Google Scholar 

  30. 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

    Article  Google Scholar 

  31. 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

    Article  Google Scholar 

  32. 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

    Article  Google Scholar 

  33. 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

    Article  Google Scholar 

  34. Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24(1):65–75

    Article  MATH  Google Scholar 

  35. 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

    Article  Google Scholar 

  36. 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

    Article  Google Scholar 

  37. 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

    Article  Google Scholar 

  38. 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

    Article  MATH  Google Scholar 

  39. 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

    Article  Google Scholar 

  40. 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

    Article  Google Scholar 

  41. 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

  42. 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

    Article  Google Scholar 

  43. 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

    Google Scholar 

  44. 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

    Chapter  Google Scholar 

  45. 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

    Article  Google Scholar 

  46. Pappis CP, Mamdani EH (1977) A fuzzy logic controller for a trafc junction. IEEE Trans Syst Man Cybern 7(10):707–717

    Article  MATH  Google Scholar 

  47. Plous S (1993) The psychology of judgment and decision making. McGraw-Hill Book Company

    Google Scholar 

  48. 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

    Article  Google Scholar 

  49. Rajesh G, Malliga P (2013) Supplier selection based on AHP QFD methodology. Proc Eng 64:1283–1292

    Article  Google Scholar 

  50. Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53:49–57

    Article  Google Scholar 

  51. 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

    Chapter  Google Scholar 

  52. Saaty TL (1980) The analytical hierarchy process, planning, priority. Resource allocation. RWS publications, USA

  53. 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

    Article  Google Scholar 

  54. Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15(3):234–281

    Article  MathSciNet  MATH  Google Scholar 

  55. Satty TL (1980) The analytic hierarchy process. Analytic Hierarchy Process

  56. Sevkli M (2010) An application of the fuzzy ELECTRE method for supplier selection. Int J Prod Res 48(12):3393–3405

    Article  MATH  Google Scholar 

  57. 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

    Article  Google Scholar 

  58. Szmidt E, Kacprzyk J (2000) Distances between intuitionistic fuzzy sets. Fuzzy Sets Syst 114(3):505–518

    Article  MathSciNet  MATH  Google Scholar 

  59. Szmidt E, Kacprzyk J (2001) Entropy for intuitionistic fuzzy sets. Fuzzy Sets Syst 118(3):467–477

    Article  MathSciNet  MATH  Google Scholar 

  60. Tam MC, Tummala VR (2001) An application of the AHP in vendor selection of a telecommunications system. Omega 29(2):171–182

    Article  Google Scholar 

  61. 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

    Article  Google Scholar 

  62. 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

  63. 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

    Article  Google Scholar 

  64. 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

    Article  Google Scholar 

  65. 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

    Article  Google Scholar 

  66. Xu Z, Liao H (2013) Intuitionistic fuzzy analytic hierarchy process. IEEE Trans Fuzzy Syst 22(4):749–761

    Article  Google Scholar 

  67. Yong D (2006) Plant location selection based on fuzzy TOPSIS. Int J Adv Manuf Technol 28(7–8):839–844

    Article  Google Scholar 

  68. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

  69. Zhao H, Guo S (2015) External benefit evaluation of renewable energy power in China for sustainability. Sustainability 7(5):4783–4805

    Article  Google Scholar 

  70. 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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Priyanka Majumder.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06326-7

Keywords

Navigation