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Graph Theory Approach to Transportation Systems Design and Optimization

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Języki publikacji
EN
Abstrakty
EN
The main aim of the paper is to present graph theory parameters and algorithms as tool to analyze and to optimise transportation systems. To realize these goals the 0-1 knapsack problem solution by SPEA algorithm, methods and procedures for finding the minimal spanning tree in graphs and digraphs, domination parameters problems accurate to analyse the transportation systems are introduced and described. Possibility of application of graph theory algorithms and parameters to analyze exemplary transportation system are shown.
Twórcy
autor
  • Gdynia Maritime University, Gdynia, Poland
Bibliografia
  • [1] COMMISSION OF THE EUROPEAN COMMUNITIES (2006), Communication from the Commission on a European Programme for Critical Infrastructure Protection, Brussels.
  • [2] Cormen, T. H. & al. (2009). Introduction to Algorithms, Third Edition. MIT Press, ISBN 0‐262‐03384‐4. Section 23.2: The algorithms of Kruskal and Prim, pp. 631–638.
  • [3] Guze, S. (2014). Application of the knapsack problem to reliability multi‐criteria optimization. Journal of Polish Safety and Reliability Association, Summer Safety and Reliability Seminars, Vol. 5, No 1, pp. 85 – 90.
  • [4] Guze, S. (2014). The graph theory approach to analyze critical infrastructures of transportation systems. Journal of Polish Safety and Reliability Association, Summer Safety and Reliability Seminars, Vol. 5, No 2, pp. 57‐62.
  • [5] Guze, S. Smolarek, L. (2011). Methods for risk minimizing in the process of decision‐making under uncertainty. Journal of Polish Safety and Reliability Association ‐ JPSRA, Vol. 2, Number 1, 123 – 128, Gdańsk‐Sopot.
  • [6] Harary, F. (1969). Graph Theory. Addison‐Wesley, Reading.
  • [7] Haynes T. W., Hedetniemi, S., Slater, P. (1988). Fundamentals of Domination in Graphs. CRC Press.
  • [8] Kołowrocki, K. (2013). Safety of critical infrastructures. Journal of Polish Safety and Reliability Association, Summer Safety and Reliability Seminars, Vol. 4, No. 1, pp. 51‐74.
  • [9] Kołowrocki, K. (2004). Reliability of Large Systems. Elsevier, Amsterdam ‐ Boston ‐ Heidelberg ‐ London ‐New York ‐ Oxford ‐ Paris ‐ San Diego ‐ San Francisco ‐Singapore ‐ Sydney ‐ Tokyo.
  • [10] Kołowrocki, K. & Soszyńska, J. (2010). Optimization of complex technical systems operation processes. Maintenance Problems, No 1, 31‐40, Radom.
  • [11] Kołowrocki, K. & Soszyńska‐Budny, J. (2011). Reliability and Safety of Complex Technical Systems and Processes, Modeling – Identification – Prediction – Optimization, Springer‐Verlag.
  • [12] Kołowrocki, K. Soszyńska‐Budny, J. (2013). Reliability prediction and optimization of complex technical systems with application in port transport. Journal of Polish Safety and Reliability Association, Summer Safety and Relibility Seminars – SSARS 2013, Volume 3, Number 1‐2, 263 – 279, Gdańsk‐Sopot.
  • [13] Leeuwen, Van, J. (1986). Graph Algorithms. Book.
  • [14] Marie, S. & Courteille, E. (2009) Multi‐Objective Optimization of Motor Vessel Route. TransNav, The International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 3, No. 2, 133‐141, Gdynia.
  • [15] Martello, S. & Toth, P. (1990). Knapsack Problems: Algorithms and Computer Implementations. Chichester, U.K.: Wiley.
  • [16] Ming‐Hua, L., Jung‐Fa, T. and Chian‐Son Y. (2012), A Review of Deterministic Optimization Methods in Engineering and Management, Mathematical Problems in Engineering, Volume 2012.
  • [17] Newell, G. F. (1980). Traffic flow on transportation networks. MIT Press Series in transportation studies, Monograph 5.
  • [18] Parekh, A. K. (1991). Analysis of Greedy Heuristic for Finding Small Dominating Sets in Graphs. Information Processing Letters, Volume 39, Issue 5, pp. 237 – 240.
  • [19] Ruan, L. & al., (2004). A greedy approximation for minimum connected dominating sets. Theoretical Computer Science, Volume 329, Issues 1–3, pp 325–330.
  • [20] Sun, W. and Yuan, Y.‐X. (2006). Optimization theory and methods: nonlinear programming. Springer‐Verlag.
  • [21] Szłapczyńska, J. (2013). Multicriteria Evolutionary Weather Routing Algorithm in Practice. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 7, No. 1, 61‐65, Gdynia.
  • [22] Venter, G. (2010). Review of Optimization Techniques, Encyclopedia of Aerospace Engineering, John Willey and Sons, Ltd.
  • [23] Zitzler, E. & Thiele, L. (1999). Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation, VOL. 3, NO. 4., 257 – 271.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f1233418-27de-46a3-ae6b-b8b173ed3f58
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