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Failure prioritization and control using the neutrosophic best and worst method

  • Melih Yucesan
  • Muhammet GulEmail author
Original Paper

Abstract

Failure prioritization process is described by identifying potential failures and its effects, quantifying their priorities and determining appropriate ways to mitigate or control. In the literature, many approaches are suggested to prioritize failures and associated effects quantitatively. Multicriteria decision-making (MCDM) approaches are forefront that they can express the failures verbally based on decision-makers’ judgments. They explain different types of uncertainties, which are generally modeled by fuzzy sets. However, fuzzy sets focus only on one membership value in decision-making. At this point, neutrosophic sets are more suitable than classical fuzzy sets by proposing three membership values named truth-membership, indeterminacy-membership and falsity-membership. Therefore, in this study, a novel approach based on the neutrosophic best and worst method (NBWM) is proposed and a case study is also performed in the implant production. The best and worst method (BWM) is merged with neutrosophic sets since it has fewer pairwise comparisons while determining the importance weights of failures. To show the applicability of the approach, a case study in an implant manufacturing plant that produces many products, including implants in different shapes and sizes in Turkey is carried out. Besides the case study, a comparative study is performed to test the validity of the proposed NBWM approach. This approach can make the decision-making process more dynamic in real-world problems with indeterminate and inconsistent information, considering the benefits of BWM and neutrosophic sets either individually or in integration. The present study contributes to the knowledge both methodologically and in an application by proposing NBWM for failure assessment problems for the first time in the literature and creating an adaptive model for manufacturing and other industries.

Keywords

Neutrosophic set Best and worst method Failure assessment Implant industry 

Notes

References

  1. Abdel-Baset M, Chang V, Gamal A, Smarandache F (2019) An integrated neutrosophic ANP and VIKOR method for achieving sustainable supplier selection: a case study in importing field. Comput Ind 106:94–110CrossRefGoogle Scholar
  2. Abdel-Basset M, Mohamed M, Zhou Y, Hezam I (2017) Multi-criteria group decision making based on neutrosophic analytic hierarchy process. J Intell Fuzzy Syst 33(6):4055–4066CrossRefGoogle Scholar
  3. Abdel-Basset M, Mohamed M, Sangaiah AK (2018a) Neutrosophic AHP-Delphi Group decision-making model based on trapezoidal neutrosophic numbers. J Ambient Intell Humanized Computing 9(5):1427–1443CrossRefGoogle Scholar
  4. Abdel-Basset M, Manogaran G, Gamal A, Smarandache F (2018b) A hybrid approach of neutrosophic sets and DEMATEL method for developing supplier selection criteria. Design Autom Embed Syst 22(3):257–278CrossRefGoogle Scholar
  5. Adem A, Çolak A, Dağdeviren M (2018) An integrated model using SWOT analysis and Hesitant fuzzy linguistic term set for evaluation occupational safety risks in life cycle of wind turbine. Saf Sci 106:184–190CrossRefGoogle Scholar
  6. Ahmad WNKW, Rezaei J, Sadaghiani S, Tavasszy LA (2017) Evaluation of the external forces affecting the sustainability of oil and gas supply chain using best worst method. J Clean Prod 153:242–252CrossRefGoogle Scholar
  7. Ahmadi HB, Kusi-Sarpong S, Rezaei J (2017) Assessing the social sustainability of supply chains using best worst method. Resour Conserv Recycl 126:99–106CrossRefGoogle Scholar
  8. Ak MF, Gul M (2019) AHP–TOPSIS integration extended with pythagorean fuzzy sets for information security risk analysis. Complex Intell Syst 5(2):113–126CrossRefGoogle Scholar
  9. Biswas P, Pramanik S, Giri BC (2015) Cosine similarity measure based multi-attribute decision-making with trapezoidal fuzzy neutrosophic numbers. Neutrosophic Sets Syst 8:46–56Google Scholar
  10. Biswas P, Pramanik S, Giri BC (2016) TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Comput Appl 27(3):727–737CrossRefGoogle Scholar
  11. Büyüközkan G, Göçer F (2019) Smart medical device selection based on intuitionistic fuzzy Choquet integral. Soft Comput 23(20):10085–10103CrossRefGoogle Scholar
  12. Can GF (2018) An intuitionistic approach based on failure mode and effect analysis for prioritizing corrective and preventive strategies. Hum Factors Ergon Manuf Serv Ind.  https://doi.org/10.1002/hfm.20729 CrossRefGoogle Scholar
  13. Chen SM, Yang MW, Yang SW, Sheu TW, Liau CJ (2012) Multicriteria fuzzy decision making based on interval-valued intuitionistic fuzzy sets. Expert Syst Appl 39(15):12085–12091CrossRefGoogle Scholar
  14. Chen SM, Cheng SH, Lan TC (2016) Multicriteria decision making based on the TOPSIS method and similarity measures between intuitionistic fuzzy values. Inf Sci 367:279–295CrossRefGoogle Scholar
  15. Chi P, Liu P (2013) An extended TOPSIS method for the multiple attribute decision making problems based on interval neutrosophic set. Neutrosophic Sets Syst 1(1):63–70Google Scholar
  16. Deli I, Subas Y (2014) Single valued neutrosophic numbers and their applications to multicriteria decision making problem. Neutrosophic Sets Syst 2(1):1–13Google Scholar
  17. Deli I, Şubaş Y (2017a) Some weighted geometric operators with SVTrN-numbers and their application to multi-criteria decision making problems. J Intell Fuzzy Syst 32(1):291–301CrossRefzbMATHGoogle Scholar
  18. Deli I, Şubaş Y (2017b) A ranking method of single valued neutrosophic numbers and its applications to multi-attribute decision making problems. Int J Mach Learn Cybern 8(4):1309–1322CrossRefGoogle Scholar
  19. Garg H (2019) Algorithms for possibility linguistic single-valued neutrosophic decision-making based on COPRAS and aggregation operators with new information measures. Measurement 138:278–290CrossRefGoogle Scholar
  20. Garg H, Nancy (2019) Multiple criteria decision making based on frank Choquet Heronian mean operator for single-valued neutrosophic sets. Appl Comput Math 18(2):163–188Google Scholar
  21. Gul M (2018a) A review of occupational health and safety risk assessment approaches based on multi-criteria decision-making methods and their fuzzy versions. Hum Ecolog Risk Assess 24(7):1723–1760CrossRefGoogle Scholar
  22. Gul M (2018b) Application of Pythagorean fuzzy AHP and VIKOR methods in occupational health and safety risk assessment: the case of a gun and rifle barrel external surface oxidation and coloring unit. Int J Occup Safety Ergon.  https://doi.org/10.1080/10803548.2018.1492251 CrossRefGoogle Scholar
  23. Gul M, Ak MF (2018) A comparative outline for quantifying risk ratings in occupational health and safety risk assessment. J Clean Prod 196:653–664CrossRefGoogle Scholar
  24. Gul M, Guneri AF (2016) A fuzzy multi criteria risk assessment based on decision matrix technique: a case study for aluminum industry. J Loss Prev Process Ind 40:89–100CrossRefGoogle Scholar
  25. Gul M, Ak MF, Guneri AF (2017a) Occupational health and safety risk assessment in hospitals: a case study using two-stage fuzzy multi-criteria approach. Hum Ecolog Risk Assess 23(2):187–202CrossRefGoogle Scholar
  26. Gul M, Celik E, Akyuz E (2017b) A hybrid risk-based approach for maritime applications: the case of ballast tank maintenance. Hum Ecol Risk Assess 23(6):1389–1403CrossRefGoogle Scholar
  27. Gul M, Guneri AF, Baskan M (2018a) An occupational risk assessment approach for construction and operation period of wind turbines. Glob J Environ Sci Manage 4(3):281–298Google Scholar
  28. Gul M, Guven B, Guneri AF (2018b) A new Fine–Kinney-based risk assessment framework using FAHP-FVIKOR incorporation. J Loss Prev Process Ind 53:3–16CrossRefGoogle Scholar
  29. Gul M, Guneri AF, Nasirli SM (2019) A fuzzy-based model for risk assessment of routes in oil transportation. Int J Environ Sci Technol 16(8):4671–4686CrossRefGoogle Scholar
  30. Gul M, Yucesan M, Serin F, Celik E (2018d) A simulation model to improve production processes in an implant manufacturing plant. In: 21st International research/expert conference”trends in the development of machinery and associated technology” TMT 2018, Karlovy Vary, Czech Republic, 18th–22nd September, (pp 177–180)Google Scholar
  31. Guo S, Zhao H (2017) Fuzzy best-worst multi-criteria decision-making method and its applications. Knowl-Based Syst 121:23–31CrossRefGoogle Scholar
  32. Guo J, Lin Z, Zu L, Chen J (2019) Failure modes and effects analysis for CO2 transmission pipelines using a hesitant fuzzy VIKOR method. Soft Comput 23(20):10321–10338CrossRefGoogle Scholar
  33. Gupta H (2018) Evaluating service quality of airline industry using hybrid best worst method and VIKOR. J Air Trans Manage 68:35–47CrossRefGoogle Scholar
  34. Gupta H, Barua MK (2016) Identifying enablers of technological innovation for Indian MSMEs using best–worst multi criteria decision making method. Technol Forecast Soc Chang 107:69–79CrossRefGoogle Scholar
  35. Gupta P, Anand S, Gupta H (2017) Developing a roadmap to overcome barriers to energy efficiency in buildings using best worst method. Sustain Cities Soc 31:244–259CrossRefGoogle Scholar
  36. Hafezalkotob A, Hafezalkotob A (2017) A novel approach for combination of individual and group decisions based on fuzzy best-worst method. Appl Soft Comput 59:316–325CrossRefzbMATHGoogle Scholar
  37. Ilbahar E, Karaşan A, Cebi S, Kahraman C (2018) A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Saf Sci 103:124–136CrossRefGoogle Scholar
  38. Karasan A, Ilbahar E, Cebi S, Kahraman C (2018) A new risk assessment approach: safety and critical effect analysis (SCEA) and its extension with pythagorean fuzzy sets. Saf Sci 108:173–187CrossRefGoogle Scholar
  39. Khanmohammadi E, Zandieh M, Tayebi T (2019) Drawing a strategy canvas using the fuzzy best-worst method. Glob J Flex Syst Manage 20(1):57–75CrossRefGoogle Scholar
  40. Kheybari S, Kazemi M, Rezaei J (2019) Bioethanol facility location selection using best-worst method. Appl Energy 242:612–623CrossRefGoogle Scholar
  41. Liang R, Wang J, Zhang H (2017a) Evaluation of e-commerce websites: an integrated approach under a single-valued trapezoidal neutrosophic environment. Knowl Based Syst 135:44–59CrossRefGoogle Scholar
  42. Liang W, Zhao G, Wu H (2017b) Evaluating investment risks of metallic mines using an extended TOPSIS method with linguistic neutrosophic numbers. Symmetry 9(8):149CrossRefGoogle Scholar
  43. Liao H, Mi X, Yu Q, Luo L (2019) Hospital performance evaluation by a hesitant fuzzy linguistic best worst method with inconsistency repairing. J Clean Prod 232:657–671CrossRefGoogle Scholar
  44. Liu P, Chen SM (2018) Multiattribute group decision making based on intuitionistic 2-tuple linguistic information. Inf Sci 430:599–619CrossRefMathSciNetGoogle Scholar
  45. Liu P, Chen SM, Liu J (2017) Multiple attribute group decision making based on intuitionistic fuzzy interaction partitioned Bonferroni mean operators. Inf Sci 411:98–121CrossRefMathSciNetGoogle Scholar
  46. Liu P, Liu J, Chen SM (2018) Some intuitionistic fuzzy Dombi Bonferroni mean operators and their application to multi-attribute group decision making. J Oper Res Soc 69(1):1–24CrossRefGoogle Scholar
  47. Luo M, Wu L, Zhou K, Zhang H (2019) Multi-criteria decision making method Based on the single valued neutrosophic sets. J Intell Fuzzy Syst 37(2):2403–2417CrossRefGoogle Scholar
  48. Malek J, Desai TN (2019) Prioritization of sustainable manufacturing barrier s using best worst method. J Clean Prod In Press.  https://doi.org/10.1016/j.jclepro.2019.04.056 CrossRefGoogle Scholar
  49. Massaglia S, Borra D, Peano C, Sottile F, Merlino VM (2019) Consumer preference heterogeneity evaluation in fruit and vegetable purchasing decisions using the best-worst approach. Foods 8(7):266CrossRefGoogle Scholar
  50. Mete S (2018) Assessing occupational risks in pipeline construction using FMEA based AHP–MOORA integrated approach under Pythagorean fuzzy environment. Hum Ecol Risk Assess.  https://doi.org/10.1080/10807039.2018.1546115 CrossRefGoogle Scholar
  51. Meticulous research center (2017) Dental implants market by material, type, structure-global forecast to 2022Google Scholar
  52. Mou Q, Xu Z, Liao H (2016) An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Inf Sci 374:224–239CrossRefGoogle Scholar
  53. Nawaz F, Asadabadi MR, Janjua NK, Hussain OK, Chang E, Saberi M (2018) An MCDM method for cloud service selection using a Markov chain and the best-worst method. Knowl Based Syst 159:120–131CrossRefGoogle Scholar
  54. Oz NE, Mete S, Serin F, Gul M (2018) Risk assessment for clearing & grading process of a natural gas pipeline project: an extended TOPSIS model with pythagorean fuzzy sets for prioritizing hazards. Hum Ecol Risk Assess. 10:11.  https://doi.org/10.1080/10807039.2018.1495057 CrossRefGoogle Scholar
  55. Ozdemir Y, Gul M, Celik E (2017) Assessment of occupational hazards and associated risks in fuzzy environment: a case study of a university chemical laboratory. Hum Ecol Risk Assess 23(4):895–924CrossRefGoogle Scholar
  56. Pamučar D, Petrović I, Ćirović G (2018) Modification of the Best-Worst and MABAC methods: a novel approach based on interval-valued fuzzy-rough numbers. Expert Syst Appl 91:89–106CrossRefGoogle Scholar
  57. Peng X, Dai J (2018) A bibliometric analysis of neutrosophic set: two decades review from 1998 to 2017. Artif Intell Rev.  https://doi.org/10.1007/s10462-018-9652-0 CrossRefGoogle Scholar
  58. Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53:49–57CrossRefGoogle Scholar
  59. Rezaei J (2016) Best-worst multi-criteria decision-making method: some properties and a linear model. Omega 64:126–130CrossRefGoogle Scholar
  60. Rezaei J, Wang J, Tavasszy L (2015) Linking supplier development to supplier segmentation using best worst method. Expert Syst Appl 42(23):9152–9164CrossRefGoogle Scholar
  61. Rezaei J, Nispeling T, Sarkis J, Tavasszy L (2016) A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. J Clean Prod 135:577–588CrossRefGoogle Scholar
  62. Salimi N, Rezaei J (2016) Measuring efficiency of university-industry Ph.D. projects using best worst method. Scientometrics 109(3):1911–1938CrossRefGoogle Scholar
  63. Shojaei P, Haeri SAS, Mohammadi S (2018) Airports evaluation and ranking model using Taguchi loss function, best-worst method and VIKOR technique. J Air Trans Manage 68:4–13CrossRefGoogle Scholar
  64. Smarandache, F. (2002). Neutrosophy and neutrosophic logic. In: First International Conference on Neutrosophy, Neutrosophic Logic, Set, Probability, and Statistics University of New Mexico, Gallup, NM (Vol 87301, pp 338–353)Google Scholar
  65. van de Kaa G, Kamp L, Rezaei J (2017) Selection of biomass thermochemical conversion technology in the Netherlands: a best worst method approach. J Clean Prod 166:32–39CrossRefGoogle Scholar
  66. Wang CY, Chen SM (2017) Multiple attribute decision making based on interval-valued intuitionistic fuzzy sets, linear programming methodology, and the extended TOPSIS method. Inf Sci 397:155–167CrossRefGoogle Scholar
  67. Wang H, Smarandache F, Zhang Y, Sunderraman R (2010) Single valued neutrosophic sets. Infinite StudyGoogle Scholar
  68. Wang H, Smarandache F, Zhang YQ (2010) Sunderraman R single valued neutrosophic sets. Multispace Multistruct 4:410–413 (Kalyan Mondal, and Surapati Pramanik) Google Scholar
  69. Wang W, Liu X, Qin Y (2018a) A fuzzy Fine-Kinney-based risk evaluation approach with extended MULTIMOORA method based on Choquet integral. Comput Ind Eng 125:111–123CrossRefGoogle Scholar
  70. Wang W, Liu X, Qin Y, Fu Y (2018b) A risk evaluation and prioritization method for FMEA with prospect theory and Choquet integral. Saf Sci 110:152–163CrossRefGoogle Scholar
  71. William MA, Devadoss AV, Sheeba JJ (2013) A study on Neutrosophic cognitive maps (NCMs) by analyzing the risk factors of breast cancer. Int J Sci Eng Res 4(2):1–4Google Scholar
  72. Yazdi M (2017) Hybrid probabilistic risk assessment using fuzzy FTA and fuzzy AHP in a process industry. J Fail Anal Prev 17(4):756–764CrossRefGoogle Scholar
  73. Ye J (2013) Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. Int J Gen Syst 42(4):386–394CrossRefMathSciNetzbMATHGoogle Scholar
  74. Ye J (2014) A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets. J Intell Fuzzy Syst 26(5):2459–2466MathSciNetzbMATHGoogle Scholar
  75. Ye J (2015a) Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses. Artif Intell Med 63(3):171–179CrossRefGoogle Scholar
  76. Ye J (2015b) Trapezoidal neutrosophic set and its application to multiple attribute decision-making. Neural Comput Appl 26(5):1157–1166CrossRefGoogle Scholar
  77. You X, Chen T, Yang Q (2016) Approach to multi-criteria group decision-making problems based on the best-worst-method and ELECTRE method. Symmetry 8(9):95CrossRefMathSciNetGoogle Scholar
  78. Yucesan M, Kahraman G (2019) Risk evaluation and prevention in hydropower plant operations: a model based on Pythagorean fuzzy AHP. Energy Policy 126:343–351CrossRefGoogle Scholar
  79. Zadeh LA (1965) Fuzzy sets. Inform Control 8(3):338–353CrossRefzbMATHGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringMunzur UniversityTunceliTurkey
  2. 2.Department of Industrial EngineeringMunzur UniversityTunceliTurkey

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