Optimization approach in multi-stop routing of small islands

• Autorzy: Krile S.

Identyfikatory

DOI

10.17402/280

• Języki publikacji: EN

Abstrakty

EN

The routing problem of small island ports is, in many cases, firmly dependent on country topology, e.g., how to connect islands with a main (home) port, where the order of stops can be different, especially if there are not enough passengers or cargo waiting to be transported to or from every port. Thus, we need a capable optimization tool with which we can adapt each route for an appropriate time schedule; for example, some routes in one cycle can touch each island (forwards or backwards) but some routes can be incomplete, to touch only a few of them. The carrier has to find space for price-cutting (lower prices per journey – more passengers on board), to be more attractive in free-market competition. In such route optimization, we have to interconnect minimal transport cost with maximal revenue (money from tickets), which could be a very demanding task (a non-linear objective cost function). Instead of a non-linear polynomial optimization, which can be very complicated and time-consuming, the network optimization methodology could be efficiently applied. The main goal is to find more efficient routes, to decrease expenses and to increase revenue at the same time (dual mini/max problem).

Słowa kluczowe

EN

non-linear transportation problem; multi-destination routing problem; capacity planning tool; shipping line scheduling; maritime traffic forecasting; competition of carriers in public transport

• Wydawca: Wydawnictwo Naukowe Akademii Morskiej w Szczecinie
• Czasopismo: Zeszyty Naukowe Akademii Morskiej w Szczecinie
• Rocznik: 2018
• Tom: nr 54 (126)
• Strony: 9--16
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Krile S.

• University of Dubrovnik, Nautical Department Ćira Carića 4, 20000 Dubrovnik, Croatia

Bibliografia

• 1. Castro, J. & Nabona, N. (1996) An Implementation of Linear and Nonlinear Multi-commodity Network Flows. European Journal of Operational Research 92, 1, pp. 37–53.
• 2. Fleisher, L. (2000) Approximating Multi-commodity Flow Independent of the Number of Commodities. Siam J. Discrete Math. 13, 4, pp. 505–520.
• 3. Foster, I. (1995) Designing and Building Parallel Programs. 3.9. Case Study: Shortest-Path Algorithms. [Online] Available from: http://www.mcs.anl.gov/~itf/dbpp/text/ node35.html#algdij1 [Accessed: November 8, 2018]
• 4. Krile, S. (2011) Logistic Support for Loading/Unloading in Shipping with Multiple Ports. Proc of 31st International Conference of Automation in Transportation (KOREMA), Pula – Milan, pp. 94–97.
• 5. Krile, S. (2013a) Efficient Heuristic for Non-linear Transportation Problem on the Route with Multiple Ports. Polish Maritime Research 20, 4, pp. 80–86. DOI 10.2478/pomr2013-0044
• 6. Krile, S. (2013b) Passage Planning with Several Ports of Loading and Discharging. Naše more 60, 1–2, pp. 21–24.
• 7. Krile, S., Krile, M. & Prusa, P. (2015) Non-Linear Minimax Problem of Multi-stop Flight Routes. Transport, Villnus 30, 4, pp. 361–371, DOI 10.3846/16484142.2015.1091984
• 8. Ouorou, A., Mahey, P. & Vial, J.Ph. (2000) A Survey of Algorithms for Convex Multi-commodity Flow Problems. Markup Languages 46, 1, pp. 126–147.
• 9. Xiea, F. & Jiab, R. (2012) Nonlinear Fixed Charge Transportation Problem by Minimum Cost Flow-based Genetic Algorithm. Computers & Industrial Engineering 63, 4, pp. 763–778.
• 10. Yan, S., Chen, H.C., Chen, Y.H. & Lou, T.C. (2007) Optimal scheduling model for ferry companies under alliances. Journal of Marine Science and Technology 15, 1, pp. 53–66.
• 11. Zangwill, W.I. (1968) Minimum Concave Cost Flows in Certain Networks. Management Science 14, 7, pp. 429–450.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).