An enhanced fuzzy evidential DEMATEL method with its application to identify critical success factors
- 189 Downloads
Due to the frequent occurrence of accidental and destructive disasters, it is essential to improve the performance of emergency systems. Facing the fact that the performance of emergency system depends on various factors and it is not feasible to optimize all these factors simultaneously due to the limitation of resources. A feasible solution is to select and improve some important factors. In this paper, a novel enhanced fuzzy evidential decision-making trial and evaluation laboratory (DEMATEL) method to identifying critical success factors (CSFs) is proposed. In the proposed method, we combine Dempster–Shafer evidence theory and DEMATEL method. Firstly, direct relations between factors are evaluated by multiple domain experts with intuitionistic fuzzy numbers (IFNs). Then, IFNs are transformed to basic probability assignments (BPAs) and can be combined by Dempster combination rule. In addition, the uncertainty and fuzziness of BPAs due to the lack of knowledge are taken into consideration to make final decision. Finally, implementing DEMATEL method, we can figure out cause–effect categories of factors with the DEMATEL method. The cause factors are identified as CSFs. The proposed method can well tackle subjectivity and fuzziness of experts evaluations. Based on the proposed method, the optimization of emergency management can be significantly simplified into optimizing CSFs. Through optimizing these CSFs, the performance of the whole systems can be significantly improved.
KeywordsEmergency management Intuitionistic fuzzy sets Dempster–Shafer evidence theory Total uncertainty measure DEMATEL
The authors are grateful to anonymous reviewers for their useful comments and suggestions on improving this paper.
The work is partially supported by National Natural Science Foundation of China (Grant Nos. 61573290, 61503237), and China Scholarship Council.
Compliance with ethical standards
Conflict of interest
Yuzhen Han declares that he has no conflict of interest. Yong Deng declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Bian T, Zheng H, Yin L, Deng Y (2018) Failure mode and effects analysis based on Dnumbers and topsis. Qual Reliab Eng Int. Article ID: QRE2268. https://doi.org/10.1002/qre.2268
- Bullen CV, Rockart JF (1981) A primer on critical success factors. Center for Information Systems Research, Sloan School of ManagementGoogle Scholar
- Cebi S, Kahraman C, Kaya I (2011) Soft computing and computational intelligent techniques in the evaluation of emerging energy technologies. In: Vasant P, Barsoum N, Webb J (eds) Innovation in power, control, and optimization: emerging energy technologies: emerging energy technologies. MIT press, USA, p 164Google Scholar
- Deng W, Yao R, Zhao H, Yang X, Li G (2017a) A novel intelligent diagnosis method using optimal ls-svm with improved pso algorithm. Soft Comput 2C4:1–18Google Scholar
- Fei L, Wang H, Chen L, Deng Y (2017) A new vector valued similarity measure for intuitionistic fuzzy sets based on OWA operators. Iran J Fuzzy Syst (accepted) Google Scholar
- Han Y, Deng Y (2018) A hybrid intelligent model for assessment of critical success factors in high-risk emergency system. J Ambient Intell Humanized Comput 1–21Google Scholar
- Han D, Han C, Yang Y (2008) A modified evidence combination approach based on ambiguity measure. In: 2008 11th international conference on Information Fusion. IEEE, pp 1–6Google Scholar
- Kang B, Deng Y (2018) Generating Z-number based on OWA weights using maximum entropy. Int J Intell Syst (accepted) Google Scholar
- Lefevre E, Colot O, Vannoorenberghe P, De Brucq D (2000) A generic framework for resolving the conflict in the combination of belief structures. In: Proceedings of the third international conference on information fusion, 2000. FUSION 2000, vol 1. IEEE, pp MOD4–MOD11Google Scholar
- Lin XH, Jia WH (2016) FMEA method combining OWA operator and fuzzy dematel. Comput SciGoogle Scholar
- Liu HC (2016) FMEA using uncertainty theories and MCDM methods. Springer, Singapore, pp 13–27Google Scholar
- Liu Z, Liu Y, Zhou K, He Y (2017) Pattern classification based on the combination of the selected sources of evidence. In: 2017 20th international conference on information fusion (Fusion). IEEE, pp 1–8Google Scholar
- Senvar O, Tuzkaya UR, Kahraman C (2014) Supply chain performance measurement: an integrated DEMATEL and fuzzy-ANP approach. In: Supply chain management under fuzziness. Springer, Berlin, pp 143–165Google Scholar
- Smarandache F, Dezert J (eds) (2015) Advances and applications of DSmT for information fusion, vol IV: collected works. Infinite StudyGoogle Scholar
- Somers TM, Nelson K (2001) The impact of critical success factors across the stages of enterprise resource planning implementations. In: Proceedings of the 34th annual Hawaii international conference on system sciences, 2001. IEEE, p 10Google Scholar
- Tolga E, Kahraman C (1991) Multi-criteria investment analysis under uncertainty. In: Technology management: the new international language. IEEE, p 396Google Scholar
- Zappini L, Marchesi S, Polo A, Viani F, Massa A (2016) Evolutionary optimization strategies applied to wireless fleet management in emergency scenarios. In: Microwave symposium, pp 1–4Google Scholar