AN EFFECTIVE METAHEURISTIC FOR TOURIST TRIP PLANNING IN PUBLIC TRANSPORT NETWORKS
Krzysztof OSTROWSKI
k.ostrowski@pb.edu.plFaculty of Computer Science, Białystok University of Technology, Wiejska 45A, 15-001 Białystok (Poland)
Abstract
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 gen-erate attractive tours in real public transport networks of Białystok and Athens. The method achieved very high-quality solutions in a short execution time.
Keywords:
time-dependent orienteering problem with time-windows, evolutionary algorithm, public transport network, tourist trip planningReferences
Campos, V., Marti, R., Sanchez-Oro, J., & Duarte, A. (2014). Grasp with Path Relinking for the Orienteering Problem. Journal of the Oper. Res. Society, 65(12), 1800–1813. https://doi.org/10.1057/jors.2013.156
DOI: https://doi.org/10.1057/jors.2013.156
Google Scholar
Chao, I., Golden, B., & Wasil, E. (1996). Theory and methodology - a fast and effective heuristic for the orienteering problem. European Journal of Operational Research, 88(3), 475–489. https://doi.org/10.1016/0377-2217(95)00035-6
DOI: https://doi.org/10.1016/0377-2217(95)00035-6
Google Scholar
Dean, B.C. (2004). Shortest paths in FIFO time-dependent networks: theory and algorithms. Technical report, MIT Department of Computer Science.
Google Scholar
Garcia, A., Vansteenwegen, P., Arbelaitz, O., Souffriau, W., & Linaza, M. T. (2013). Integrating public transportation in personalised electronic tourist guides. Computers and Operations Research, 40(3), 758–774. https://doi.org/10.1016/j.cor.2011.03.020
DOI: https://doi.org/10.1016/j.cor.2011.03.020
Google Scholar
Gavalas, D., Konstantopoulos, C., Mastakas, K., Pantziou, G., & Vathis, N. (2015). Heuristics for the time dependent team orienteering problem: Application to tourist route planning. Computers and Operation Research, 62, 36-50. https://doi.org/10.1016/j.cor.2015.03.016
DOI: https://doi.org/10.1016/j.cor.2015.03.016
Google Scholar
Gendreau, M., Laporte, G., & Semet, F. (1998). A tabu search heuristic for the undirected selective travelling salesman problem. European Journal of Operational Research, 106(2–3), 539–545. https://doi.org/10.1016/S0377-2217(97)00289-0
DOI: https://doi.org/10.1016/S0377-2217(97)00289-0
Google Scholar
Golden, B., Levy, L., & Vohra, R. (1987). The orienteering problem. Naval Research Logistics, 34, 307-318. https://doi.org/10.1002/1520-6750(198706)34:3<307::AID-NAV3220340302>3.0. CO;2-D
DOI: https://doi.org/10.1002/1520-6750(198706)34:3<307::AID-NAV3220340302>3.0.CO;2-D
Google Scholar
Gunawan, A., Yuan, Z., & Lau, H. C. (2014). A Mathematical Model and Metaheuristics for Time Dependent Orienteering Problem. In PATAT 2014: Proceedings of the 10th International Conference of the Practice and Theory of Automated Timetabling, 26–29 August 2014 (pp. 202–217). Research Collection School Of Information Systems.
Google Scholar
Mahfoud, S. W. (1992). Crowding and preselection revisited. In Proceedings of the 2nd International Conference on Parallel Problem Solving from Nature (PPSN II), Brussels, Belgium, 1992 (pp. 27–36). Amsterdam: Elsevier.
Google Scholar
Ostrowski, K. (2015). Parameters Tuning of Evolutionary Algorithm for the Orienteering Problem. Advances in Computer Science Research, 12, 53–78.
Google Scholar
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. https://doi.org/10.1007/s10479-016-2278-1
DOI: https://doi.org/10.1007/s10479-016-2278-1
Google Scholar
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. https://doi.org/10.1007/978-3-319-59105-6_5
DOI: https://doi.org/10.1007/978-3-319-59105-6_5
Google Scholar
Schilde, M., Doerner, K., Hartl, R., & Kiechle, G. (2009). Metaheuristics for the biobjective orienteering problem. Swarm Intelligence, 3(3), 179–201. https://doi.org/10.1007/s11721-009-0029-5
DOI: https://doi.org/10.1007/s11721-009-0029-5
Google Scholar
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.
Google Scholar
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. https://doi.org/10.1016/j.ejor.2008.02.037
DOI: https://doi.org/10.1016/j.ejor.2008.02.037
Google Scholar
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. https://doi.org/10.1016/j.ejor.2013.11.038
DOI: https://doi.org/10.1016/j.ejor.2013.11.038
Google Scholar
Authors
Krzysztof OSTROWSKIk.ostrowski@pb.edu.pl
Faculty of Computer Science, Białystok University of Technology, Wiejska 45A, 15-001 Białystok Poland
Statistics
Abstract views: 49PDF downloads: 31
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Qingyu Liu, Roben A. Juanatas, MASK FACE INPAINTING BASED ON IMPROVED GENERATIVE ADVERSARIAL NETWORK , Applied Computer Science: Vol. 19 No. 2 (2023)
- Martin KRAJČOVIČ, Patrik GRZNÁR, UTILISATION OF EVOLUTION ALGORITHM IN PRODUCTION LAYOUT DESIGN , Applied Computer Science: Vol. 13 No. 3 (2017)
- Puppala Praneeth, Majety Sathvika, Vivek Kommareddy, Madala Sarath, Saran Mallela, Koneru Suvarna Vani, Prasun Chkrabarti, CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK , Applied Computer Science: Vol. 19 No. 2 (2023)
- Manikandan SRIDHARAN, Delphin Carolina RANI ARULANANDAM, Rajeswari K CHINNASAMY, Suma THIMMANNA, Sivabalaselvamani DHANDAPANI, RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING , Applied Computer Science: Vol. 17 No. 2 (2021)
- Jarosław GIL, Andrzej POLAŃSKI, APPLICATION OF GILLESPIE ALGORITHM FOR SIMULATING EVOLUTION OF FITNESS OF MICROBIAL POPULATION , Applied Computer Science: Vol. 18 No. 4 (2022)
- Łukasz WOJCIECHOWSKI, Tadeusz CISOWSKI, MODEL OF A COMPUTER SYSTEM FOR SELECTION OF OPERATING PARAMETERS FOR TRANSPORT VEHICLES IN THE ASPECT OF THEIR DURABILITY , Applied Computer Science: Vol. 14 No. 4 (2018)
- Nawazish NAVEED, Hayan T. MADHLOOM, Mohd Shahid HUSAIN, BREAST CANCER DIAGNOSIS USING WRAPPER-BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORK , Applied Computer Science: Vol. 17 No. 3 (2021)
- Jolanta Brzozowska, Arkadiusz Gola, COMPUTER AIDED ASSEMBLY PLANNING USING MS EXCEL SOFTWARE – A CASE STUDY , Applied Computer Science: Vol. 17 No. 2 (2021)
- Zaid ALSAYGH, Zohair AL-AMEEN, CONTRAST ENHANCEMENT OF SCANNING ELECTRON MICROSCOPY IMAGES USING A NONCOMPLEX MULTIPHASE ALGORITHM , Applied Computer Science: Vol. 18 No. 2 (2022)
- Hamid JAN, Amjad ALI, OPTIMIZATION OF FINGERPRINT SIZE FOR REGISTRATION , Applied Computer Science: Vol. 15 No. 2 (2019)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.