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Using a FMEA–TIFIAD Approach to Identify the Risk of Railway Dangerous Goods Transportation System

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Abstract

In this paper a systemic approach that combines Failure Mode and Effect Analysis (FMEA) and trapezoidal intuitionistic fuzzy information axiomatic design (TIFIAD) is proposed to identify the risk of railway dangerous goods transportation system (RDNGTS), which has seven steps: (1) identify potential risk factors and risk sub-indicators of RDNGTS; (2) define the failure criterion of severity of failures (S), portability of occurrence (O) and possibility of detection (D) of FMEA based on trapezoidal intuitionistic fuzzy numbers (TrIFNs); (3) invite experts to score the TrIFNs of S, O and D for each risk sub-indicator; (4) aggregate the TrIFNs of S, O and D for each risk sub-indicator; (5) calculate the TIFIAD of each risk sub-indicator; (6) calculate the total TIFIAD of each risk sub-indicator; (7) calculate the total TIFIAD of each risk factor. Compared with FMEA, FMEA–TIFIAD has the following improvements: (1) The TrIFNs are applied to score the S, O and D of each risk sub-indicator of RDNGTS; (2) The TIFIAD of the S, O and D are calculated for each risk sub-indicator, the product of the S, O and D could replace the Risk Priority Number (RPN) of FMEA; (3) The Entropy Weight Method is used to calculate the weight of each risk sub-indicator. The calculation results show that the potential human risk should be paid more attentions; the FMEA–TIFIAD is more reliable and accurate than FMEA. Finally the improvement suggestions for managers and operators of RDNGTS are given.

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References

  • Alam S, Lokan C, Aldis G, Barry S, Butcher R, Abbass H (2013) Systemic identification of airspace collision risk tipping points using an evolutionary multi-objective scenario-based methodology. Transp Res Part C Emerg Technol 35(9):57–84

    Google Scholar 

  • Albee A, Battel S, Brace R et al (2000) Report on the loss of the mars polar lander and deep space 2 missions. Jet Propulsion Laboratory, Pasadena

    Google Scholar 

  • Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20:87–96

    Google Scholar 

  • Bendul JC, Skorna ACH (2016) Exploring impact factors of shippers’ risk prevention activities: a European survey in transportation. Transp Res Part E Logist Transp Rev 90(5):206–223

    Google Scholar 

  • Bhandari J, Abbassi R, Garaniya V, Khan F (2015) Risk analysis of deep water drilling operations using Bayesian network. J Loss Prev Process Ind 38:11–23

    Google Scholar 

  • Cansu D, Elifcan G, Müfide N, Ali K (2016) Classical and fuzzy fmea risk analysis in a sterilization unit. Comput Ind Eng 101:286–294

    Google Scholar 

  • Chang KH, Cheng CH, Chang YC (2008) Reliability assessment of an aircraft propulsion system using IFS and OWA tree. Eng Optim 40(10):907–921

    Google Scholar 

  • Chen XK, Zhu HL, Chen JQ (2011) Hazard identification and control in the pre-blasting process. J Coal Sci Eng 17(3):331–335

    Google Scholar 

  • Chi G, Qi F, Li G (2013) The evaluation model of scientific development concept for Chinese provinces based on combination weighting of improved Group-G1 and its application. Syst Eng-Theory Pract 33(6):1448–1457

    Google Scholar 

  • Crawley F, Tyler B (2015) HAZOP: guide to best practice: guidelines to best practice for the process and chemical industries, 3rd edn. Elsevier, Amsterdam

    Google Scholar 

  • de Bakker K, Boonstra A, Wortmann H (2014) The communicative effect of risk identification on project success. Int J Proj Organ Manag 6:138–156

    Google Scholar 

  • Feili HR, Akar N, Lotfizadeh H, Bairampour M, Nasiri S (2012) Risk analysis of geothermal power plants using failure modes and effects analysis (FMEA) technique. Energy Convers Manag 72:69–76

    Google Scholar 

  • Fidanova S, Atanassov K, Dimov I (2017) Generalized nets as a tool for modelling of railway networks. In: Advanced computing in industrial mathematics. Springer International Publishing, pp 23–35

  • Garai A, Mandal P, Roy TK (2016) Intuitionistic fuzzy t-sets based optimization technique for production-distribution planning in supply chain management. Opsearch 53(4):950–975

    Google Scholar 

  • Garai A, Mandal P, Roy TK (2017) Multipollutant air quality management strategies: t-sets based optimization technique under imprecise environment. Int J Fuzzy Syst 19(6):1927–1939

    Google Scholar 

  • Gheorghe AV, Birchmeier J, Vamanu D, Papazoglou I, Kröger W (2005) Comprehensive risk assessment for rail transportation of dangerous goods: a validated platform for decision support. Reliab Eng Syst Saf 88(3):247–272

    Google Scholar 

  • Govindan K, Chaudhuri A (2016) Interrelationships of risks faced by third party logistics service providers: a dematel based approach. Transp Res Part E Logist Transp Rev 90:177–195

    Google Scholar 

  • Gregoriades A, Mouskos KC (2013) Black spots identification through a Bayesian networks quantification of accident risk index. Transp Res Part C Emerg Technol 28(3):28–43

    Google Scholar 

  • Guo X, Zhang L, Liang W, Haugen S (2018) Risk identification of third-party damage on oil and gas pipelines through the Bayesian network. J Loss Prev Process Ind 54:163–178

    Google Scholar 

  • Hong ES, Lee IM (2009) Quantitative risk evaluation based on event tree analysis technique: application to the design of shield TBM. Tunn Undergr Space Technol 25(24):269–277

    Google Scholar 

  • Hsu WKK, Huang SHS, Tseng WJ (2016) Evaluating the risk of operational safety for dangerous goods in airfreights—a revised risk matrix based on fuzzy AHP. Transp Res Part D 48:235–247

    Google Scholar 

  • Huang WC, Shuai B (2017) Using improved entropy-cloud model to select high-speed railway express freight train service sites. Math Probl Eng 2017(6):1–13

    Google Scholar 

  • Huang WC, Shuai B, Jing Z, Lei W, Jie M (2016a) Corrected entropy based operation performance evaluation about urban rail transportation non-networks system. J Transp Syst Eng Inf Technol 16(6):115–121

    Google Scholar 

  • Huang WC, Shuai B, Pang L, Yang ZQ (2016b) Research on coupling coordination degree based method for assessing risk in road dangerous goods transport system. China Saf Sci J 26(6):117–122

    Google Scholar 

  • Huang WC, Shuai B, Sun Y, Li ML, Pang L (2018a) Using entropy-TOPSIS-coupling and coordination model to evaluate railway dangerous goods transportation system risk. China Saf Sci J 28(2):134–138

    Google Scholar 

  • Huang WC, Shuai B, Zhang GY (2018b) Using improved WBS-RBS to identify the risk of railway dangerous goods transportation process. China Saf Sci J 28(8):93–99

    Google Scholar 

  • Huang WC, Shuai B, Sun Y, Wang Y, Antwi E (2018c) Using entropy-TOPSIS method to evaluate urban rail transit system operation performance: the China case. Transp Res Part A Policy Pract 111:292–303

    Google Scholar 

  • Huang WC, Shuai B, Sun Y (2019a) Study on coupling risk formation mechanism of railway dangerous goods transportation system based on N-K model. J China Railw Soc 41(5):1–9

    Google Scholar 

  • Huang WC, Shuai B, Zuo BR, Xu YF, Antwi E (2019b) A systematic railway dangerous goods transportation system risk analysis approach: the 24 model. J Loss Prev Process Ind 61:94–103

    Google Scholar 

  • Jagtman HM, Hale AR, Heijer T (2006) Ex ante assessment of safety issues of new technologies in transport. Transp Res Part A Policy Pract 40(6):459–474

    Google Scholar 

  • Kahraman C, Cebi S, Onar SC, Oztaysi B (2018) A novel trapezoidal intuitionistic fuzzy information axiom approach: an application to multicriteria landfill site selection. Eng Appl Artif Intell 67:157–172

    Google Scholar 

  • Kulak O, Kahraman C (2005) Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process. Inf Sci 170(2):191–210

    Google Scholar 

  • Kulba V, Bakhtadze N, Zaikin O, Shelkov A, Chernov I (2017) Scenario analysis of management processes in the prevention and the elimination of consequences of man-made disasters. Proc Comput Sci 112:2066–2075

    Google Scholar 

  • Lakshmana GNV, Jeevaraj S, Dhanasekaran P (2016) A linear ordering on the class of trapezoidal intuitionistic fuzzy numbers. Expert Syst Appl 60:269–279

    Google Scholar 

  • Li X, Chen X (2017) Value determination method based on multiple reference points under a trapezoidal intuitionistic fuzzy environment. Appl Soft Comput 63:39–49

    Google Scholar 

  • Li J, Zhang H, Han YS, Wang BD (2016) Study on failure of third-party damage for urban gas pipeline based on fuzzy comprehensive evaluation. PLoS ONE 11:e0166472

    Google Scholar 

  • Lin SS, Li CB, Xu FQ, Liu D, Liu JC (2018) Risk identification and analysis for new energy power system in china based on d numbers and decision-making trial and evaluation laboratory (DEMATEL). J Clean Prod 180:81–96

    Google Scholar 

  • Long J (2017) Research on risk investigation of major fire in buildings based on safety checklist method. Jiangxi Chem Eng 2:134–140

    Google Scholar 

  • Mishra AR, Rani P (2018a) Interval-valued intuitionistic fuzzy waspas method: application in reservoir flood control management policy. Group Decis Negot 27(6):1047–1078

    Google Scholar 

  • Mishra AR, Rani P (2018b) Biparametric information measures-based todim technique for interval-valued intuitionistic fuzzy environment. Arab J Sci Eng 43(6):3291–3309

    Google Scholar 

  • Mishra AR, Chandel A, Motwani D (2018) Extended mabac method based on divergence measures for multi-criteria assessment of programming language with interval-valued intuitionistic fuzzy sets. Granul Comput 5:1–21

    Google Scholar 

  • Mishra AR, Rani P, Mardani A, Pardasani KR, Govindan K, Alrasheedi M (2019a) Healthcare evaluation in hazardous waste recycling using novel interval-valued intuitionistic fuzzy information based on complex proportional assessment method. Comput Ind Eng. https://doi.org/10.1016/j.cie.2019.106140

    Article  Google Scholar 

  • Mishra AR, Sisodia G, Pardasani KR, Sharma K (2019b) Multi-criteria IT personnel selection on intuitionistic fuzzy information measures and ARAS methodology. Iran J Fuzzy Syst. https://doi.org/10.22111/ijfs.2019.27737.4871

    Article  Google Scholar 

  • Mishra AR, Rani P, Pardasani KR, Mardani A (2019c) A novel hesitant fuzzy WASPAS method for assessment of green supplier problem based on exponential information measures. J Clean Prod. https://doi.org/10.1016/j.jclepro.2019.117901

    Article  Google Scholar 

  • Peeters JFW, Basten RJI, Tinga T (2018) Improving failure analysis efficiency by combining fta and fmea in a recursive manner. Reliab Eng Syst Saf 172:36–44

    Google Scholar 

  • Powell JH, Mustafee N, Chen AS, Hammond M (2016) System-focused risk identification and assessment for disaster preparedness: dynamic threat analysis. Eur J Oper Res 254(2):550–564

    Google Scholar 

  • Qin Q, Liang F, Li L, Chen YW, Yu GF (2017) A todim-based multi-criteria group decision making with triangular intuitionistic fuzzy numbers. Appl Soft Comput 55:93–107

    Google Scholar 

  • Rani P, Jain D (2019) Information measures-based multi-criteria decision-making problems for interval-valued intuitionistic fuzzy environment. Proc Natl Acad Sci, India, Sect A. https://doi.org/10.1007/s40010-019-00597-5

    Article  Google Scholar 

  • Rani P, Jain D, Hooda DS (2018) Shapley function based interval-valued intuitionistic fuzzy VIKOR technique for correlative multi-criteria decision making problems. Iran J Fuzzy Syst 15(1):25–54

    Google Scholar 

  • Saini N, Bajaj RK, Gandotra N, Dwivedi RP (2018) Multi-criteria decision making with trapezoidal intuitionistic fuzzy number based on distance measure and parametric entropy approach. Proc Comput Sci 125:34–41

    Google Scholar 

  • Sheehan B, Murphy F, Ryan C, Mullins M, Liu HY (2017) Semi-autonomous vehicle motor insurance: a Bayesian network risk transfer approach. Transp Res Part C Emerg Technol 82:124–137

    Google Scholar 

  • Shu MH, Cheng CH, Chang JR (2006) Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly. Microelectron Reliab 46(12):2139–2148

    Google Scholar 

  • Studic M, Majumdar A, Schuster W, Ochieng WY (2017) A systemic modelling of ground handling services using the functional resonance analysis method. Transpor Res Part C 74:245–260

    Google Scholar 

  • Suh NP (1990) The principles of design. Oxford University Press, New York

    Google Scholar 

  • Suh NP (2001) Axiomatic design: advances and applications. Oxford University Press, New York

    Google Scholar 

  • Tsai MC (2006) Constructing a logistics tracking system for preventing smuggling risk of transit containers. Transp Res Part A Policy Pract 40(6):526–536

    Google Scholar 

  • Valmohammadi C, Dashti S (2016) Using interpretive structural modeling and fuzzy analytical process to identify and prioritize the interactive barriers of e-commerce implementation. Inf Manag 53(2):157–168

    Google Scholar 

  • Wang WJ, Luoh L (2000) Simple computation for the defuzzifications of center of sum and center of gravity. J Intell Fuzzy Syst 9:53–59

    Google Scholar 

  • Wang JQ, Nie R, Zhang HY, Chen XH (2013) New operators on triangular intuitionistic fuzzy numbers and their applications in system fault analysis. Inf Sci 251(12):79–95

    Google Scholar 

  • Yan D, Chi G, He Y (2010) Study on index weighting method based on improved group-G2. J Syst Eng 25(4):540–546

    Google Scholar 

  • Yang Z, Yang Z, Yin J (2018) Realising advanced risk-based port state control inspection using data-driven Bayesian networks. Transp Res Part A Policy Pract 110:38–56

    Google Scholar 

  • Yip TL (2008) Port traffic risks-a study of accidents in Hong Kong waters. Transp Res Part E Logist Transp Rev 44(5):921–931

    Google Scholar 

  • Zhang X, Liu P (2010) Method for aggregating trapezoidal fuzzy intuitionistic fuzzy information and its application to decision making. Technol Econ Dev Econ 16(2):280–290

    Google Scholar 

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Acknowledgements

This research was jointly supported by the Traffic and Transportation Engineering Experiment and Comprehensive Innovation Center, School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan. Subsidized by the National Natural Science Foundation of China (71173177), Youth Fund of National Natural Science Foundation of China (72001179), State Railway Administration Science and Technology Project (KF2014-041), the Southwest Jiaotong University 2015 Graduate Innovative Experimental Practice Program (YC201507103), and Southwest Jiaotong University 2018 Postgraduate Academic Literacy Improvement Plan (2018KXK04). The authors would like to thank the anonymous referees for their valuable comments and suggestions.

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Huang, W., Li, Y., Kou, X. et al. Using a FMEA–TIFIAD Approach to Identify the Risk of Railway Dangerous Goods Transportation System. Group Decis Negot 30, 63–95 (2021). https://doi.org/10.1007/s10726-020-09706-x

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