An effective metaheuristic for tourist trip planning in public transport networks

• Autorzy: Ostrowski Krzysztof

Identyfikatory

DOI

10.23743/acs-2018-09

• Języki publikacji: EN

Abstrakty

EN

The Time-Dependent Orienteering Problem with Time Windows (TDOPTW) is a combinatorial optimization problem defined on graphs. Its real life applications are particularly associated with tourist trip planning in trans-port networks, where travel time between two points depends on the moment of travel start. In the paper an effective TDOPTW solution (evolutionary algorithm with local search operators) was presented and applied to generate attractive tours in real public transport networks of Białystok and Athens. The method achieved very high-quality solutions in a short execution time.

Słowa kluczowe

EN

evolutionary algorithm; public transport network; tourist trip planning

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2018
• Tom: Vol. 14, no 2
• Strony: 5--19
• Opis fizyczny: Bibliogr. 16 poz., fig., tab.

Twórcy

autor

Ostrowski Krzysztof

• Faculty of Computer Science, Białystok University of Technology, Wiejska 45A, 15-001 Białystok, Poland

Bibliografia

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• [10] Ostrowski, K. (2015). Parameters Tuning of Evolutionary Algorithm for the Orienteering Problem. Advances in Computer Science Research, 12, 53–78.
• [11] Ostrowski, K., Karbowska-Chilinska, J., Koszelew, J., & Zabielski, P. (2017). Evolution-inspired local improvement algorithm solving orienteering problem. Annals of Operations Research, 253(1), 519-543. doi:10.1007/s10479-016-2278-1
• [12] Ostrowski, K. (2017). Evolutionary Algorithm for the Time-Dependent Orienteering Problem. In K. Saeed, W. Homenda, & R. Chaki (Eds.), Computer Information Systems and Industrial Management. CISIM 2017, Lecture Notes in Computer Science (10244, pp. 50–62). Cham: Springer. doi:10.1007/978-3-319-59105-6_5
• [13] Schilde, M., Doerner, K., Hartl, R., & Kiechle, G. (2009). Metaheuristics for the biobjective orienteering problem. Swarm Intelligence, 3(3), 179–201. doi:10.1007/s11721-009-0029-5
• [14] Tasgetiren, M. (2001). A genetic algorithm with an adaptive penalty function for the orienteering problem. Journal of Economic and Social Research, 4(2), 1–26.
• [15] Vansteenwegen, P., Souffriau, W., Vanden Berghe, G., & Oudheusden, D.V. (2009). A guided local search metaheuristic for the team orienteering problem. European Journal of the Operational Research, 196(1), 118–127. doi:10.1016/j.ejor.2008.02.037
• [16] Verbeeck, C., Sörensen, K., Aghezzaf, E.H., & Vansteenwegen, P. (2013). A fast solution method for the time-dependent orienteering problem. European Journal of Operational Research, 236(2), 419–432. doi:10.1016/j.ejor.2013.11.038

Uwagi

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