Advertisement

Dimensioning of Multiple Capacity Transport Line with Mutual Traffic Correlation

  • Srećko KrileEmail author
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 32)

Abstract

Transport networks need very effective optimization tool for good utilization of transport line capacities. Planning and dimensioning of capacities can be done in definite planning horizon or to satisfy offered traffic from point to point in the network, crossing multiple lines on the path. Such approach is the crucial part of every Intelligent Transport System (ITS) today. Capacity dimensioning is an important element of resource management and it can be seen as CEP (capacity expansion problem). The mathematical model for optimal capacity sizing of N different transport types (capacity types—commodities) is explained, minimizing the total expansion cost. In the case of CEP for multiple line capacities with mutual traffic correlation such problem could be more demanding. With such approach an efficient heuristic algorithm for three different capacity types is being developed and tested on two different scenarios, for long-term capacity planning and for strategic multi-stop route creation in airline industry.

Keywords

Traffic Demand Capacity State Positive Flow Line Capacity Expansion Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Brimberg J, Love FR (1998) Solving a class of two-dimensional uncapacitated location-allocation problems by dynamic programming. Oper Res 46(5):702–709zbMATHMathSciNetCrossRefGoogle Scholar
  2. 2.
    Klincewicz JG, Luss H, Yu C-S (1988) A large-scale multi-location capacity planning model. Eur J Oper Res 34(2):178–190zbMATHCrossRefGoogle Scholar
  3. 3.
    Krile S, Kos M (2001) Satellite link capacity planning in mobile networks. In: Proceedings of ICT’01, Bukurest, pp 151–156Google Scholar
  4. 4.
    Krile S (2005) The resource management for mobile networks. In: Proceedings of 23rd ICSSC’05 (International communications satellite systems conference), Book of synopses, pp 52, Rome, ItalyGoogle Scholar
  5. 5.
    Lee S-B, Luss H (1987) Multifacility-type capacity expansion planning: algorithms and complexities. Oper Res 35(2):249–253zbMATHCrossRefGoogle Scholar
  6. 6.
    Li S, Tirupat D (l994) Capacity expansion problem with multiple products: technology selection and timing of capacity additions. Oper Res 42(5):958–976Google Scholar
  7. 7.
    Luss H (1983) A multifacility capacity expansion model with joint expansion set-up costs. Naval Res Logistic Q 30:111–970CrossRefGoogle Scholar
  8. 8.
    Castro J, Nabona N (1996) An implementation of linear and nonlinear multi-commodity network flows. Eur J Oper Res 92(1):37–53zbMATHCrossRefGoogle Scholar
  9. 9.
    Chang S, Gavish B (1995) Lower bounding procedures for multi-period telecommunications network expansion problems. Oper Res 43(1):43–57zbMATHCrossRefGoogle Scholar
  10. 10.
    Luss H (1986) A heuristic for capacity expansion planning with multiple facility types. Naval Res Logistics Q 33(04):685–701zbMATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    Rajagopalan S (1998) Capacity expansion and equipment replacement: a unified approach. Oper Res 46:846–857zbMATHCrossRefGoogle Scholar
  12. 12.
    Sutter A, Vanderbeck F, Wolsey L (1998) Optimal placement of add/drop multiplexers: heuristic and exact algorithms. Oper Res 46:719–728zbMATHCrossRefGoogle Scholar
  13. 13.
    Van Mieghem JA (1998) Investment strategies for flexible resources. Manage Sci 44(8):1071–1078zbMATHCrossRefGoogle Scholar
  14. 14.
    Zangwill WI (1968) Minimum concave cost flows in certain networks. Manage Sci 14:429–450zbMATHMathSciNetCrossRefGoogle Scholar
  15. 15.
    Krile S (2005) Optimal voyage planning in container shipping. In: 25th international conference of automation in transportation. Zagreb, Copenhagen, p 32Google Scholar
  16. 16.
    Krile S (2003) A heuristic approach to satellite link capacity planning applied in mobile networks. Promet-Traffic-Traffico, vol 15. Portorož, Trieste, Zagreb, pp 19–29Google Scholar
  17. 17.
    Zenzerovic Z, Beslic M (2003) Contribution to the optimisation of the cargo transportation problem. Promet-Traffic-Traffico, vol 15, No. 1. Portoroz, Trieste, Zagreb, pp 13–17Google Scholar
  18. 18.
    Wollmer R (1970) Multicommodity supply and transportation networks with resource constraints: The generalized multi-commodity flow problem. The Rand Corporation, Retrieved from http://www.rc.rand.org/pubs/research_memoranda/2009/RM6143.pdf
  19. 19.
    Yan S, Tseng CH (2002) A passenger demand model for airline flight scheduling and fleet routing. Comput Oper Res 29(11):1559–1581zbMATHCrossRefGoogle Scholar
  20. 20.
    Chang S, Schonfeld P (2004) Optimized schedules for airline routes. J Transp Eng. doi:  10.1061/(ASCE)0733-947X(2004)130:4(412). Retrieved from Wallace database. Croatia Airlines Web Site. Retrieved from http://www.croatiaairlines.com/LinkClick.aspx?fileticket=W%2boFWLEaQQ4%3d&tabid=296
  21. 21.
    Yan S, Young HF (1996) A decision support framework for multi-fleet routing and multi-stop flight scheduling. Transp Res 30(5):379–398Google Scholar
  22. 22.
    Yang S, Tang CH, Lee M (2007) CA flight scheduling model for Taiwan airlines under market competitions. Int J Manage Sci 35(1):61–74Google Scholar
  23. 23.
    Garaix T, Artiques C, Feillet D, Josselin D (2009) Vehicle routing problems with alternative paths: an application to on-demand transportation. Eur J Oper Res 204(1):62–75CrossRefGoogle Scholar
  24. 24.
    Stojković G, Soumis F, Desrosiers J, Solomon MM (2002) An optimization model for a real-time flight scheduling problem. Transp Res Part A 36:779–788CrossRefGoogle Scholar
  25. 25.
    Givoni M, Rietveld P (2006) Choice of airplane size—explanations and implications. Paper provided by Tinbergen Institute with number 06-113/3. Retrieved from http://ideas.repec.org/p/dgr/uvatin/20060113.html
  26. 26.
    Gomm K (2005) Predictive planning aids route profitability at BA. Comput Wkly:8. Retrieved from Wallace databaseGoogle Scholar
  27. 27.
    Tatalović M, Babić ŠR, Bajić J (2009) Airline route profitability modeling. Paper presented at 12th international conference on transport science ICTS. Retrieved from https://bib.irb.hr/datoteka/417860
  28. 28.
    Barnhart C, Marla L, Jiang H (2009) Optimization approaches to airline industry challenges: airline schedule planning and recovery. Paper presented on Dagstuhl SeminarGoogle Scholar
  29. 29.
    Carey S (2007) Calculating costs in the clouds; how flight-planning software helps airlines balance fuel, distance, wind, ‘overfly’ fees. Wall Street J. Retrieved from http://www.fwz.aero/news/calulationg-costs-in-the-clouds.html
  30. 30.
    Ouorou A, Mahey P, Vial JPh (2000) A survey of algorithms for convex multicommodity flow problems. Markup Lang 46(1):126–147zbMATHGoogle Scholar
  31. 31.
    Trochim WMK (2008) Research methods knowledge base. Retrieved from http://www.socialresearchmethods.net/
  32. 32.
    Yan S, Chen HC, Chen YH, Lou TC (2007) Optimal scheduling models for ferry companies under alliances. J Mar Sci Technol 15(1):53–66Google Scholar
  33. 33.
    Yan S, Chen HL (2002) A scheduling model and a solution algorithm for inter-city bus carriers. Transp Res Part A Policy Pract 36(9):805–825CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Maritime, Department of Electrical Engineering and ComputingUniversity of DubrovnikDubrovnikCroatia

Personalised recommendations