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Warianty tytułu
Języki publikacji
Abstrakty
The aim of this paper is to present the concept of the supply chain resilience assessment in the case of disruptive events occurrence. Firstly, the methods for modelling uncertainty in terms of their application to assess this type of risk will be discussed, and then the concept of a fuzzy logic expert model enabling a quantitative assessment of supply chain resilience will be presented. Finally the structure of the simulation model has been proposed, which consists of the partial resilience models, namely: security, survivability and recovery ones. In the course of the simulation process, it is possible to identify the rules involved in system output as well as changes in resilience level which account for changes in inputs values.
Słowa kluczowe
Rocznik
Tom
Strony
31--38
Opis fizyczny
Bibliogr. 21 poz., rys.
Twórcy
autor
- The University of Dąbrowa Górnicza, Dąbrowa Górnicza, Poland
autor
- AGH University of Science and Technology, Cracow, Poland
Bibliografia
- [1] AMBER (2009) Assessing, Measuring, and Benchmarking Resilience, FP7 – 216295.
- [2] Bukowski, L., Feliks, J. (2005). Application of Fuzzy Sets in Evaluation of Failure Likelihood. Proceedings of the 18-th International Conference on Systems Engineering – Las Vegas, IEEE CS.
- [3] Bukowski, L. & Feliks, J. (2011). Evaluation of Technical Systems Dependability with the Use of Fuzzy Logic and Experts’ Knowledge. Proc. of the 15th World Multi-Conference on Systemics, Cybernetics and Informatics, Orlando (Florida).
- [4] Christopher, M. & Peck, H. (2004). Building the resilient supply chain. The Int. Journal of Logistics Management, Vol. 15, No. 2.
- [5] Herrera, I. A. & Hovden, J. (2008). The Leading indicators applied to maintenance in the framework of resilience engineering: A conceptual approach. 3rd Resilience Engineering Symposium, Antibes- Juan Les Pins, France.
- [6] Klir, G. J. (2004). Generalized information theory: aims, results and open problems. Reliability Engineering and System Safety 85, 341-354.
- [7] Klir, G. J. & Yuan, B. (1995). Fuzzy sets and fuzzy logic: theory and applications. Upper Saddle River, NJ: Prentice Hall, PTR.
- [8] Mendel, J. M. (2001). Uncertain rule-based fuzzy logic systems. Upper Saddle River, NJ: Prentice Hall, PTR.
- [9] Ponomarov, S.Y., Holcomb, M.C. (2009). Understanding the concept of supply chain resilience. Int. Journal of Logistics Management, Vol. 20. No. 1.
- [10] ReSIST, (2009) Deliverable D39: Selected Current Practices.
- [11] Shannon, C. E. (1948). The mathematical theory of communication. Bell System Tech. J. 27, 379423.
- [12] Sheffi, Y. (2001). Supply chain management under the threat of international terrorism. Int. Journal of Logistics Management, Vol. 12. No. 2.
- [13] Sheffi, Y. & Rice, J.B. (2005). A supply chain view of the resilient enterprise. Sloan Management Review, Vol. 47, No. 1.
- [14] Taleb, N.N. (2010). The Black Swan: the impact of the highly improbable. London: Penguin Books.
- [15] Utkin, L.V. (2004). A new efficient algorithm for computing the imprecise reliability of monotone systems. Reliability Engineering and System Safety 86, 179-190.
- [16] Walley, P. (1991). Statistical reasoning with imprecise probabilities. London; Chapman and Hall.
- [17] Westrum, R. (2006). A Typology of Resilience Situations, in Resilience Engineering. Concepts and Precepts, E. Hollnagel, D. D. Woods, and N. Leveson, Eds. Aldershot, UK: Ashgate.
- [18] WinFACT (2003) User Guide.
- [19] Zadeh, L. A. (1965). Fuzzy sets. Inform. Control,; 8, 338-353.
- [20] Zadeh, L.A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Systems; 1, 3-28.
- [21] Zadeh, L.A. (2005). Toward a generalized theory of uncertainty (GTU) – an outline. Information Sciences 1721-40.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-437d1394-bd72-4b11-be91-1315e685432b