Failure mode and effect analysis approach considering risk attitude of dynamic reference point cumulative prospect theory in uncertainty contexts
Tóm tắt
Failure mode and effect analysis (FMEA) method is widely utilized as a powerful reliability management tool to effectively evaluate and prevent risk problems that occur in all aspects of production, service and transportation. Since FMEA experts have different professional backgrounds and the particularity of the risk assessment environment, they may show different risk attitudes and bounded rational behavior. Thus, this paper develops an FMEA framework based on the dynamic reference point cumulative prospect theory considering risk attitude. The linguistic distribution assessment (LDA) and linguistic scale function are utilized to indicate the risk attitude based personalized FMEA experts’ evaluation information. Further, based on the idea of maximizing deviation and the LDA-EMD (earth mover’s distance) formula, the criteria weight determination method considering the different risk attitudes of the FMEA expert is constructed. Then, a dynamic reference point cumulative prospect theory considering different risk attitudes is developed to obtain the prospect value of the failure modes (FMs) under each FMEA expert. The comprehensive expert weight determination method is established which takes the subjective and objective aspects of expert evaluation and revision factor into account. Finally, the numerical example is carried out to verify the validity and superiority performance of the proposed FMEA method. The improved FMEA method can enhance the flexibility and reliability of risk assessment. Findings proved that it is necessary to consider the different and dynamic risk attitudes of experts in the practical risk assessment.
Tài liệu tham khảo
Abrahamsen HB, Abrahamsen EB, Høyland S (2016) On the need for revising healthcare failure mode and effect analysis for assessing potential for patient harm in healthcare processes. Reliab Eng Syst Saf 155:160–168
Ahsen A (2008) Cost-oriented failure mode and effects analysis. Int J Qual Reliab Manage 25(5):466–476
Bowles JB, Pelaez CE (1995) Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliab Eng Syst Saf 50(2):203–213
Can GF (2018) An intutionistic approach based on failure mode and effect analysis for prioritizing corrective and preventive strategies. Hum Factors Ergon Manuf 28(3):130–147
Carpitella S, Certa A, Izquierdo J et al (2018) A combined multi-criteria approach to support FMECA analyses: a real-world case. Reliab Eng Syst Saf 169:394–402
Chi CF, Sigmund D, Astardi MO (2020) Classification scheme for root cause and failure modes and effects analysis (FMEA) of passenger vehicle recalls. Reliab Eng Syst Saf 200:106929
Fang H, Li J, Song W (2019) Failure mode and effects analysis: an integrated approach based on rough set theory and prospect theory. Soft Comput 24(9):6673–6685
Fang XB (2015) Identification of risks for cold chain logistics failure. Tech Econ Manag Res 08:26–30 ((in Chinese))
Fattahi R, Khalilzadeh M (2018) Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment. Saf Sci 102:290–300
Fattahi R, Tavakkoli-Moghaddam R, Khalilzadeh M, Shahsavari-Pour N, Soltani R (2020) A novel FMEA model based on fuzzy multiple-criteria decision-making methods for risk assessment. J Enterp Inf Manag 33(5):881–904
Fattahi R, Tavakkoli-Moghaddam R, Khalilzadeh M, Shahsavari-Pour N, Soltani R (2021) Risk assessment by a new FMEA model based on an extended AHP method under a fuzzy environment. Environ Energy Econ Res 5(4):1–14
Fu Y, Qin Y, Wang WZ et al (2020) An extended FMEA model based on cumulative prospect theory and type-2 intuitionistic fuzzy VIKOR for the railway train risk prioritization. Entropy 22(12):1418
Gao JW, Li M, Liu HH (2015) Generalized ordered weighted utility averaging-hyperbolic absolute risk aversion operators and their applications to group decision-making. Eur J Oper Res 243(1):258–270
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
Ghoushchi SJ, Khazaeili M (2019) G-Numbers: importance-necessity concept in uncertain environment. Int J Manag Fuzzy Syst 5(1):27–32
Guerrero HH, Bradley JR (2013) Failure modes and effects analysis: An evaluation of group versus individual performance. Prod Oper Manag 22(6):1524–1539
Helvacioglu S, Ozen E (2014) Fuzzy based failure modes and effect analysis for yacht system design. Ocean Eng 79:131–141
Huang J, Li ZS, Liu HC (2017) New approach for failure mode and effect analysis using linguistic distribution assessments and TODIM method. Reliab Eng Syst Saf 167:302–309
Huang J, You JX, Liu HC et al (2020) Failure mode and effect analysis improvement: a systematic literature review and future research agenda. Reliab Eng Syst Saf 199:106885
Huang G, Xiao L (2021) Failure mode and effect analysis: An interval-valued intuitionistic fuzzy cloud theory-based method. Appl Soft Comput 98:106834
Huang G, Xiao L, Pedrycz W et al (2022a) Failure mode and effect analysis using T-spherical fuzzy maximizing deviation and combined comparison solution methods. IEEE Trans Reliab. https://doi.org/10.1109/TR.2022.3194057
Huang G, Xiao L, Pedrycz W et al (2022b) Design alternative assessment and selection: a novel Z-cloud rough number-based BWM-MABAC model. Inf Sci 603:149–189
Li J, Fang H, Song WY (2019) Failure mode and effects analysis using variable precision rough set theory and TODIM method. IEEE Trans Reliab 68(4):1242–1256
Liao HC, Qin R, Gao CY et al (2019) Score-HeDLiSF: A score function of hesitant fuzzy linguistic term set based on hesitant degrees and linguistic scale functions: an application to unbalanced hesitant fuzzy linguistic MULTIMOORA. Inf Fusion 48:39–54
Liu HC, Li ZJ, Song WY et al (2017) Failure mode and effect analysis using cloud model theory and PROMETHEE method. IEEE Trans Reliab 66(4):1058–1072
Liu HC, Liu L, Lin QL (2013) Fuzzy failure mode and effects analysis using fuzzy evidential reasoning and belief rule-based methodology. IEEE Trans Reliab 62(1):23–36
Liu HC, Wang LE, Li ZW et al (2019a) Improving risk evaluation in FMEA with cloud model and hierarchical TOPSIS method. IEEE Trans Fuzzy Syst 27(1):84–95
Liu HC, You JX, Chen SM et al (2016) An integrated failure mode and effect analysis approach for accurate risk assessment under uncertainty. IIE Trans 48(11):1027–1042
Liu HC, You JX, Duan CY (2019b) An integrated approach for failure mode and effect analysis under interval-valued intuitionistic fuzzy environment. Int J Prod Econ 207:163–172
Liu HC, You XY, Tsung F et al (2018) An improved approach for failure mode and effect analysis involving large group of experts: An application to the healthcare field. Qual Eng 30(4):762–775
Liu PD, Li Y (2021) An improved failure mode and effect analysis method for multi-criteria group decision-making in green logistics risk assessment. Reliab Eng Syst Saf 215:107826
Liu Y, Fan ZP, Zhang Y (2014) Risk decision analysis in emergency response: a method based on cumulative prospect theory. Comput Oper Res 42(2):75–82
Lo HW, Liou JJH (2018) A novel multiple-criteria decision-making-based FMEA model for risk assessment. Appl Soft Comput 73:684–696
Lo HW, Liou JJH, Huang CN et al (2019) A novel failure mode and effect analysis model for machine tool risk analysis. Reliab Eng Syst Saf 183:173–183
Merigo JM, Gil-Lafuente AM (2013) Induced 2-tuple linguistic generalized aggregation operators and their application in decision-making. Inf Sci 236:1–16
Nguyen TL, Shu MH, Hsu BM (2016) Extended FMEA for sustainable manufacturing: an empirical study in the non-woven fabrics industry. Sustainability 8(9):939
Nie RX, Tian ZP, Wang XK et al (2018) Risk evaluation by FMEA of supercritical water gasification system using multi-granular linguistic distribution assessment. Knowl-Based Syst 162:185–201
Pratt J (1964) Risk aversion in the small and in the large. Econometrica 32:122–136
Qin JD, Xi Y, Pedrycz W (2020) Failure mode and effects analysis (FMEA) for risk assessment based on interval type-2 fuzzy evidential reasoning method. Appl Soft Comput 89:106134
Rubner Y, Tomasi C, Guibas LJ (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput vis 40(2):99–121
Seiti H, Hafezalkotob A, Martínez L (2019) R-numbers, a new risk modeling associated with fuzzy numbers and its application to decision making. Inf Sci 483:206–231
Song W, Zhu JJ (2019) A multistage risk decision making method for normal cloud model considering behavior characteristics. Appl Soft Comput 78:393–406
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
Tooranloo HS, Ayatollah AS (2016) A model for failure mode and effects analysis based on intuitionistic fuzzy approach. Appl Soft Comput 49:238–247
Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47(2):263–291
Tversky A, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain 5(4):297–323
Wang WZ, Liu XW, Qin Y et al (2018a) A risk evaluation and prioritization method for FMEA with prospect theory and Choquet integral. Saf Sci 110:152–163
Wang Z, Gao JM, Wang RX et al (2018b) Failure Mode and Effects Analysis by Using the House of Reliability-Based Rough VIKOR Approach. IEEE Trans Reliab 67(1):230–248
Xiao L, Huang G, Pedrycz W et al (2022a) A q-rung orthopair fuzzy decision-making model with new score function and best-worst method for manufacturer selection. Inf Sci 608:153–177
Xiao L, Huang G, Zhang G (2022b) An integrated risk assessment method using Z-fuzzy clouds and generalized TODIM. Qual Reliab Eng Int 38(4):1909–1943
Yao SB (2018) A new distance-based consensus reaching model for multi-attribute group decision-making with linguistic distribution assessments. Int J Comput Intell Syst 12(1):395–409
Yeh TM, Chen LY (2014) Fuzzy-based risk priority number in FMEA for semiconductor wafer processes. Int J Prod Res 52(2):539–549
Yu SM, Wang J, Wang JQ et al (2018) A multi-criteria decision-making model for hotel selection with linguistic distribution assessments. Appl Soft Comput 67:741–755
Zadeh LA (2011) A note on Z-numbers. Inf Sci 181(14):2923–2932
Zhang GQ, Dong YC, Xu YF (2014) Consistency and consensus measures for linguistic preference relations based on distribution assessments. Inf Fusion 17:46–55
Zhang Z, Guo CH, Martínez L (2017) Managing multi-granular linguistic distribution assessments in large-scale multi-attribute group decision making. IEEE Trans Syst Man Cybern Syst 47(11):3063–3076
Zhou YQ, Xia JW, Zhong YT et al (2016) An improved FMEA method based on the linguistic weighted geometric operator and fuzzy priority. Qual Eng 28(4):491–498
Zhu JH, Shuai B, Li GF et al (2020) Failure mode and effect analysis using regret theory and PROMETHEE under linguistic neutrosophic context. J Loss Prev Process Ind 64:104048