Dependability of discrete transport system : model, simulation, measures

• Autorzy: Mazurkiewicz J.
• Języki publikacji: EN

Abstrakty

EN

The paper proposes a method of reliability and functional analysis related to discrete transport systems. The proposed analysis is based on modelling and simulating of the system behaviour. Monte Carlo simulation is used for proper reliability and functional parameters calculation. The simulator is built using Scalable Simulation Framework (SSF). No restriction on the system structure and on a kind of distribution is the main advantage of the method. The paper presents some exemplar system modelling. The authors stress the problem of influence of the reliability parameters for final functional measures (required time of delivery) – the key value to calculate the availability of the system. They also propose to measure the economic quality of discrete transport system by “profit function”. The presented problem is practically essential for defining an organization of vehicle maintenance and transportation system logistics.

Słowa kluczowe

EN

discrete transport system; system dependability; functional model; dependability model; Monte-Carlo simulation

• Wydawca: Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Journal of Polish Safety and Reliability Association
• Rocznik: 2011
• Tom: Vol. 2, No. 1
• Strony: 143--152
• Opis fizyczny: Bibliogr. 21 poz., rys., wykr.

Twórcy

autor

Mazurkiewicz J.

• Wrocław University of Technology, Poland

Bibliografia

• [1] Barcelo, J., Codina, E., Casas, J., Ferrer, J.L. & Garcia, D. (2005). Microscopic Traffic Simulation: a Tool for the Design, Analysis And Evaluation Of Intelligent Transport Systems. Journal of Intelligent and Robotic Systems: Theory and Applications, Vol. 41, 173-203.
• [2] Barlow, R. & Proschan, F. (1996). Mathematical Theory of Reliability. Philadelphia: Society for Industrial and Applied Mathematics.
• [3] Ben-Akiva, M., Cuneo, D., Hasan, M., Jha, M. & Yang, Q. (2003). Evaluation of Freeway Control Using a Microscopic Simulation Laboratory. Transportation Research, Part C (Emerging Technologies), Vol. 11C, 29-50.
• [4] Birta, L. & Arbez, G. (2007). Modelling and Simulation: Exploring Dynamic System Behaviour. London: Springer.
• [5] Burt, C.N. & Caccetta, L. (2007). Match Factor for Heterogeneous Truck and Loader Fleets. International Journal of Mining, Reclamation and Environment, Vol. 21, 262-270.
• [6] Duinkerken, M.B., Dekker, R., Kurstjens, S.T.G.L., Ottjes, J.A., & Dellaert, N.P. (2006). Comparing Transportation Systems for InterTerminal Transport at the Maasvlakte Container Terminals. OR Spectrum, Vol. 28, 469-493.
• [7] Fishman, G. (1996). Monte Carlo: Concepts, Algorithms, and Applications. Springer-Verlag.
• [8] Gartner, N., Messer, C.J. & Rathi, A.K. (1998). Traffic Flow Theory and Characteristics. In: T.R. Board (Ed.). Texas: University of Texas at Austin.
• [9] Gold, N., Knight, C., Mohan, A. & Munro, M. (2004). Understanding service-oriented software. IEEE Software, Vol. 21, 71-77.
• [10] Ioannou, P.A. (2008). Intelligent Freight Transportation. Carolina: Taylor and Francis Group.
• [11] Krzyzanowska, J. (2007). The Impact of Mixed Fleet Hauling on Mining Operations at Venetia Mine. Journal of The South African Institute of Mining and Metallurgy, Vol. 107, 215-224.
• [12] Liu, H., Chu, L. & Recker, W. (2004). Performance Evaluation of ITS Strategies Using Microscopic Simulation. Proc. of the 7th International IEEE Conference on Intelligent Transportation Systems, 255-270.
• [13] Sanso, B. & Milot, L. (1999). Performability of a Congested Urban-Transportation Network when Accident Information is Available. Transportation Science, Vol. 33, No 1, 10-21.
• [14] Taylor, M.A.P., Woolley, J.E. & Zito, R. (2000). Integration of the Global Positioning System and Geographical Information Systems for Traffic Congestion Studies. Transportation Research, Part C (Emerging Technologies), Vol. 8C, 257-285.
• [15] Vis, I.F.A. (2006). Survey of Research in the Design and Control of Automated Guided Vehicle Systems. European Journal of Operational Research, Vol. 170, 677-709.
• [16] Walkowiak, T. & Mazurkiewicz, J. (2009). Analysis of Critical Situations in Discrete Transport Systems. International Conference on Dependability of Computer Systems, IEEE Computer Society Press, 364-371.
• [17] Walkowiak, T. & Mazurkiewicz, J. (2008). Availability of Discrete Transport System Simulated by SSF Tool. International Conference on Dependability of Computer Systems, IEEE Computer Society Press, 430-437.
• [18] Walkowiak, T. & Mazurkiewicz, J. (2008). Functional Availability Analysis of Discrete Transport System Realized by SSF Simulator. Computational Science – ICCS 2008, SpringerVerlag, LNCS 5101, Part I, 671-678.
• [19] Walkowiak, T. & Mazurkiewicz, J. (2010). Algorithmic Approach to Vehicle Dispatching in Discrete Transport Systems. Technical approach to dependability. Wroclaw, 173-188.
• [20] Walkowiak, T. & Mazurkiewicz, J. (2010). Functional Availability Analysis of Discrete Transport System Simulated by SSF Tool. International Journal of Critical Computer-Based Systems, Vol. 1, No 1-3, 255-266.
• [21] Walkowiak, T. & Mazurkiewicz, J. (2010). Soft Computing Approach to Discrete Transport System Management. LNAI 6114, 675-682.