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Computer Support of Decision-Making for the Planning Movement of Freight Wagons on the Rail Network

  • Marianna JacynaEmail author
  • Mirosław Krześniak
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 21)

Abstract

The article presents selected aspects of the problem of supporting the planning of freight wagons on the rail network. It has been pointed out that one of the problems in freight transport is the proper planning of operational activities of rail carriers. The problem is difficult because of its complexity. Planning the movement of freight wagons should take into account the need not only to transport loaded wagons, but also empty ones. The complexity of the problem stems from a number of factors, which should be considered during transport planning. These factors can be divided into technical, organizational or economic. The analysis allowed to develop a mathematical model for planning the movement of loaded and empty wagons, which includes: data schemes, decision variables, boundary conditions and constraints, criterion functions, as well as traffic disruptions in the rail network. Consequently, it was possible to develop a heuristic algorithm and a simulation tool, in the form of the ModPCar application. The subject matter is connected mainly with business needs of railway carriers. The effect, in the form of an IT tool, facilitating the process of decision support in the organization of rail transport, may be used, among others, by rail freight carriers. This paper describes the stages of the ant algorithm i.e. the stage of designating the probability of transition of ants to the other points of the route, update pheromone. In this paper the process of calibration of this algorithm was presented. The results of the ant algorithm were compared with the random results.

Keywords

Railway transport planning Compact system Railway operational service Wagons rotation 

References

  1. 1.
    Ambroziak, T., Jacyna, M.: Queueing theory approach to transport process dynamics, Part 1. Dynamics of transport network connections. Arch. Transp. 14(4), 5–20 (2002)Google Scholar
  2. 2.
    Jacyna, M.: Multicriteria evaluation of traffic flow distribution in a multimodal transport corridor, taking into account logistics base service. Arch. Transp. 10(1–2), 43–66 (1999)Google Scholar
  3. 3.
    Law Office of the Parliament: Uniform Text of the Act of 28 March 2003 on Rail Transport (Journal of Laws of 2013, Item 1594) with Amendments. Law Office of the Parliament, Warsaw (2003)Google Scholar
  4. 4.
    Midedelkoop, D., Bouwman, M.: Train network simulator for support of network wide planning of infrastructure and timetables. In: Brebbia, C.A., Allan, J., Hill, R.J., Sciutto, G., Sone, S. (eds.) Computer in Railways VII, pp. 267–276. WitPress, Southampton (2000)Google Scholar
  5. 5.
    Cacchiani, V., Caprara, A., Toth, P.: Scheduling extra freight trains on railway networks. Transp. Res. Part B 44(2), 215–231 (2010)CrossRefGoogle Scholar
  6. 6.
    Mu, S., Dessouky, M.: Scheduling freight trains traveling on complex networks. Transp. Res. Part B 45(7), 1103–1123 (2011)CrossRefGoogle Scholar
  7. 7.
    Vansteenwegen, P., Van Oudheusden, D.: Developing railway timetables which guarantee a better service. Eur. J. Oper. Res. 173(1), 337–350 (2006)CrossRefzbMATHGoogle Scholar
  8. 8.
    Caprara, A., Kroon, L., Monaci, M., Peeters, M., Toth, P.: Passenger railway optimization. In: Handbooks in Operations Research and Management Science, vol. 14, pp. 129–187. Elsevier, Amsterdam (2007)Google Scholar
  9. 9.
    Goossens, J., Van Hoesel, C., Kroon, L.: On solving multi-type railway line planning problems. Eur. J. Oper. Res. 168, 403–424 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Kuo, A., Miller-Hooks, E., Mahmassani, H.: Freight train scheduling with elastic demand. Transp. Res. Part E 46(6), 1057–1070 (2010)CrossRefGoogle Scholar
  11. 11.
    Mees, A.I.: Railway scheduling by network optimization. Math. Comput. Model. 15(1), 33–42 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Shi, M., Maged, D.: Scheduling Freight trains traveling on complex networks. Transp. Res. Part B 45(7), 1103–1123 (2011)CrossRefGoogle Scholar
  13. 13.
    Wolfenburg, A.: New version of the BBS method and its usage for determining and scheduling vehicle routes. Arch. Transp. 31(3), 83–91 (2014)CrossRefGoogle Scholar
  14. 14.
    Glover, F.: Future paths for integer programming and links to Artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Steuer, R.: An interactive multiple objective linear programming procedure. TIMS Stud. Manag. Sci. 6, 225–239 (1977)Google Scholar
  16. 16.
    Zeleny, M.: Multiple Criteria Decision Making. McGraw-Hill, New York (1982)zbMATHGoogle Scholar
  17. 17.
    Roy, B.: Decision-aid and decision making. Eur. J. Oper. Res. 45(2–3), 324–331 (1990)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Korhonen, P., Laakso, J.: A visual interactive method for solving the multicriteria problem. Eur. J. Oper. Res. 24(2), 277–287 (1986)CrossRefzbMATHGoogle Scholar
  19. 19.
    Garcia, B.L., Potvin, J.Y., Rousseau, J.M.: A parallel implementation of the tabu search heuristic for vehicle routing problems with time window constraints. Comput. Oper. Res. 21(9), 1025–1033 (1994)CrossRefzbMATHGoogle Scholar
  20. 20.
    Fügenschuh, A., Homfeld, H., Schülldorf, H.: Single-car routing in rail freight transport. Transp. Sci. 49(1), 130–148 (2013)CrossRefGoogle Scholar
  21. 21.
    Jacyna-Gołda, I., Izdebski, M., Podviezko, A.: Assessment of the efficiency of assignment of vehicles to tasks in supply chains: a case-study of a municipal company. Transport 31(4), 1–9 (2016)Google Scholar
  22. 22.
    Jacyna-Gołda, I., Lewczuk, K., Szczepański, E., Murawski, J.: Computer aided implementation of logistics processes—selected aspects. In: Mikulski, J. (ed.) Tools of Transport Telematics. CCIS, vol. 531, pp. 67–80. Springer, Switzerland (2015)CrossRefGoogle Scholar
  23. 23.
    Jacyna-Gołda, I.: Decision-making model for supporting supply chain efficiency evaluation. Arch. Transp. 33(1), 17–31 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of TransportWarsaw University of TechnologyWarsawPoland
  2. 2.PKP CARGO S.A. PolandWarsawPoland

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