A hybrid intelligent model for assessment of critical success factors in high-risk emergency system
- 287 Downloads
High-risk emergency systems are emerging as a new generation technology to prevent disasters. Latest research points out that these systems could protect properties and lives in an efficient way. Limited to the sources, the feasible way to improve the performance of the system is to identify critical success factors (CSFs) and then optimize them. In this paper, a multi-criteria decision-making (MCDM) approach integrating Affinity Diagram, Decision Making Trial and Evaluation Laboratory (DEMATEL), fuzzy cognitive map (FCM) and Dempster–Shafer evidence theory (evidence theory) is proposed to identify critical success factors in high-risk emergency system. The DEMATEL and FCM are initially combined to tackle the decision-making problem in theory and practice. This model has ability to fuse technical, economic, political and social attributes. The proposed method is applied to select CSFs for Chongqing city.
KeywordsDEMATEL Fuzzy cognitive map Dempster–Shafer evidence theory Critical success factors High-risk emergency system Multi-criteria decision making
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).
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.
- Bullen CV, Rockart JF (1981) A primer on critical success factorsGoogle Scholar
- Cavaliere D, Senatore S, Loia V (2018) Proactive uavs for cognitive contextual awareness. IEEE Syst JGoogle Scholar
- Dempster AP (2008) A generalization of bayesian inference. Classic works of the Dempster–Shafer theory of belief functions 219:73–104Google Scholar
- Deng X, Deng Y (2018) D-AHP method with different credibility of information. Soft Comput. https://doi.org/10.1007/s00500-017-2993-9
- 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
- Fontela E, Gabus A (1976) The dematel observerGoogle Scholar
- Foster ST, Ganguly KK (2007) Managing quality: Integrating the supply chain. Pearson Prentice Hall Upper Saddle River, New JerseyGoogle Scholar
- Fujita H, Gaeta A, Loia V, Orciuoli F (2018) Resilience analysis of critical infrastructures: a cognitive approach based on granular computing. IEEE Trans CybernGoogle Scholar
- Gabus A, Fontela E (1973) Perceptions of the world problematique: Communication procedure, communicating with those bearing collective responsibility. Battelle Geneva Research Centre, Geneva, SwitzerlandGoogle Scholar
- Kang B, Deng Y (2018) Generating Z-number based on OWA weights usingmaximum entropy. Int J Intell Syst (accepted) Google Scholar
- Kang B, Chhipi-Shrestha G, Deng Y, Mori J, Hewage K, Sadiq R (2017) Development of a predictive model for clostridium difficile infection incidence in hospitals using gaussian mixture model and Dempster–Shafer theroy. Stochastic Environ Res Risk Assess. https://doi.org/10.1007/s00477-017-1459-z (accepted) CrossRefGoogle Scholar
- Liu HC (2016) Fmea combining vikor, dematel, and ahp methods. In: FMEA using uncertainty theories and MCDM methods. Springer, pp 199–213Google Scholar
- Liu Z, Pan Q, Dezert J, Martin A (2017b) Combination of classifiers with optimal weight based on evidential reasoning. IEEE Trans Fuzzy SystGoogle Scholar
- Nacházel T (2015) Optimization of decision-making in artificial life model based on fuzzy cognitive maps. In: 2015 International Conference on Intelligent Environments (IE), IEEE, pp 136–139Google Scholar
- O’keefe J, Nadel L (1978) The hippocampus as a cognitive mapGoogle Scholar
- Papageorgiou E, Stylios C, Groumpos P (2003) Fuzzy cognitive map learning based on nonlinear Hebbian rule. In: Australasian Joint Conference on Artificial Intelligence. Springer, pp 256–268Google Scholar
- Rathore S, Loia V, Park JH (2017) Spamspotter: an efficient spammer detection framework based on intelligent decision support system on facebook. Appl Soft ComputGoogle Scholar
- Salmeron JL, Palos-Sanchez PR (2017) Uncertainty propagation in fuzzy grey cognitive maps with hebbian-like learning algorithms. IEEE Trans CybernGoogle Scholar
- Somers TM, Nelson K (2001) The impact of critical success factors across the stages of enterprise resource planning implementations. In: System Sciences, 2001. Proceedings of the 34th Annual Hawaii International Conference on, IEEE, pp 10Google Scholar
- Tsai SB, Xue YZ, Huang PY, Zhou J, Li GD, Guo WF, Lau H, Shang ZW (2014) Establishing a criteria system for green production. Proc Inst Mech Eng Part B J Eng Manuf, p 0954405414535923Google 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
- Zhou NY, Yuen KKF (2014) Towards a hybrid approach of primitive cognitive office analysis. In: IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEE, Beijing, China, pp 1049–1053Google Scholar
- Zhou X, Hu Y, Deng Y, Chan FTS, Ishizaka A (2016) A dematel-based completion method for incomplete pairwise comparison matrix in ahp. Ann Oper Res 1–22Google Scholar