IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i12p2193-d459438.html
   My bibliography  Save this article

Time-Dependent Theme Park Routing Problem by Partheno-Genetic Algorithm

Author

Listed:
  • Zhang Yang

    (Department of Information Engineering, Faculty of Science and Engineering, Hosei University, Tokyo 184-8584, Japan)

  • Jiacheng Li

    (Department of Electrical, Electronics and Information Engineering, Faculty of Engineering, Kanagawa University, Yokohama 221-8686, Japan)

  • Lei Li

    (Department of Information Engineering, Faculty of Science and Engineering, Hosei University, Tokyo 184-8584, Japan)

Abstract

With the improvement of people’s living standards and entertainment interests, theme parks have become one of the most popular holiday places. Many theme park websites provide a variety of information, according to which tourists can arrange their own schedules. However, most theme park websites usually have too much information, which makes it difficult for tourists to develop a tourism planning. Therefore, the theme park routing problem has attracted the attention of scholars. Based on the Traveling Salesman Problem (TSP) network, we propose a Time-Dependent Theme Park Routing Problem (TDTPRP), in which walking time is time-dependent, considering the degree of congestion and fatigue. The main goal is to maximize the number of attractions visited and satisfaction and to reduce queues and walking time. To verify the feasibility and the effectiveness of the model, we use the Partheno-Genetic Algorithm (PGA) and an improved Annealing Partheno-Genetic Algorithm (APGA) to solve the model in this paper. Then, in the experimental stage, we conducted two experiments, and the experimental data were divided into real-world problem instances and randomly generated problem instances. The results demonstrate that the parthenogenetic simulated annealing algorithm has better optimization ability than the general parthenogenetic algorithm when the data scale is expanded.

Suggested Citation

  • Zhang Yang & Jiacheng Li & Lei Li, 2020. "Time-Dependent Theme Park Routing Problem by Partheno-Genetic Algorithm," Mathematics, MDPI, vol. 8(12), pages 1-20, December.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2193-:d:459438
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/12/2193/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/8/12/2193/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vansteenwegen, Pieter & Souffriau, Wouter & Oudheusden, Dirk Van, 2011. "The orienteering problem: A survey," European Journal of Operational Research, Elsevier, vol. 209(1), pages 1-10, February.
    2. Vicky Mak & Tommy Thomadsen, 2006. "Polyhedral combinatorics of the cardinality constrained quadratic knapsack problem and the quadratic selective travelling salesman problem," Journal of Combinatorial Optimization, Springer, vol. 11(4), pages 421-434, June.
    3. Gendreau, Michel & Laporte, Gilbert & Semet, Frederic, 1998. "A tabu search heuristic for the undirected selective travelling salesman problem," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 539-545, April.
    4. John D. C. Little & Katta G. Murty & Dura W. Sweeney & Caroline Karel, 1963. "An Algorithm for the Traveling Salesman Problem," Operations Research, INFORMS, vol. 11(6), pages 972-989, December.
    5. Elizabeth L. Bouzarth & Richard J. Forrester & Kevin R. Hutson & Rahul Isaac & James Midkiff & Danny Rivers & Leonard J. Testa, 2018. "A Comparison of Algorithms for Finding an Efficient Theme Park Tour," Journal of Applied Mathematics, Hindawi, vol. 2018, pages 1-14, October.
    6. Dominique Feillet & Pierre Dejax & Michel Gendreau, 2005. "Traveling Salesman Problems with Profits," Transportation Science, INFORMS, vol. 39(2), pages 188-205, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rahma Lahyani & Mahdi Khemakhem & Frédéric Semet, 2017. "A unified matheuristic for solving multi-constrained traveling salesman problems with profits," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(3), pages 393-422, September.
    2. Angelelli, E. & Archetti, C. & Vindigni, M., 2014. "The Clustered Orienteering Problem," European Journal of Operational Research, Elsevier, vol. 238(2), pages 404-414.
    3. Bian, Zheyong & Liu, Xiang, 2018. "A real-time adjustment strategy for the operational level stochastic orienteering problem: A simulation-aided optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 246-266.
    4. Ido Orenstein & Tal Raviv & Elad Sadan, 2019. "Flexible parcel delivery to automated parcel lockers: models, solution methods and analysis," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 683-711, December.
    5. Racha El-Hajj & Rym Nesrine Guibadj & Aziz Moukrim & Mehdi Serairi, 2020. "A PSO based algorithm with an efficient optimal split procedure for the multiperiod vehicle routing problem with profit," Annals of Operations Research, Springer, vol. 291(1), pages 281-316, August.
    6. Zhang, Shu & Ohlmann, Jeffrey W. & Thomas, Barrett W., 2014. "A priori orienteering with time windows and stochastic wait times at customers," European Journal of Operational Research, Elsevier, vol. 239(1), pages 70-79.
    7. Michael D. Moskal & Erdi Dasdemir & Rajan Batta, 2023. "Unmanned Aerial Vehicle Information Collection Missions with Uncertain Characteristics," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 120-137, January.
    8. Krzysztof Ostrowski & Joanna Karbowska-Chilinska & Jolanta Koszelew & Pawel Zabielski, 2017. "Evolution-inspired local improvement algorithm solving orienteering problem," Annals of Operations Research, Springer, vol. 253(1), pages 519-543, June.
    9. Daniel Negrotto & Irene Loiseau, 2021. "A Branch & Cut algorithm for the prize-collecting capacitated location routing problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 34-57, April.
    10. Archetti, Claudia & Corberán, Ángel & Plana, Isaac & Sanchis, José Maria & Speranza, M. Grazia, 2015. "A matheuristic for the Team Orienteering Arc Routing Problem," European Journal of Operational Research, Elsevier, vol. 245(2), pages 392-401.
    11. Pěnička, Robert & Faigl, Jan & Saska, Martin, 2019. "Variable Neighborhood Search for the Set Orienteering Problem and its application to other Orienteering Problem variants," European Journal of Operational Research, Elsevier, vol. 276(3), pages 816-825.
    12. Lei, Chao & Lin, Wei-Hua & Miao, Lixin, 2014. "A multicut L-shaped based algorithm to solve a stochastic programming model for the mobile facility routing and scheduling problem," European Journal of Operational Research, Elsevier, vol. 238(3), pages 699-710.
    13. Wouter Souffriau & Pieter Vansteenwegen & Greet Vanden Berghe & Dirk Van Oudheusden, 2013. "The Multiconstraint Team Orienteering Problem with Multiple Time Windows," Transportation Science, INFORMS, vol. 47(1), pages 53-63, February.
    14. Dolinskaya, Irina & Shi, Zhenyu (Edwin) & Smilowitz, Karen, 2018. "Adaptive orienteering problem with stochastic travel times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 1-19.
    15. Sun, Peng & Veelenturf, Lucas P. & Hewitt, Mike & Van Woensel, Tom, 2018. "The time-dependent pickup and delivery problem with time windows," Transportation Research Part B: Methodological, Elsevier, vol. 116(C), pages 1-24.
    16. Ávila, Thais & Corberán, Ángel & Plana, Isaac & Sanchis, José M., 2016. "A branch-and-cut algorithm for the profitable windy rural postman problem," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1092-1101.
    17. Ann Campbell & Michel Gendreau & Barrett Thomas, 2011. "The orienteering problem with stochastic travel and service times," Annals of Operations Research, Springer, vol. 186(1), pages 61-81, June.
    18. Balcik, Burcu, 2017. "Site selection and vehicle routing for post-disaster rapid needs assessment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 30-58.
    19. Stavropoulou, F. & Repoussis, P.P. & Tarantilis, C.D., 2019. "The Vehicle Routing Problem with Profits and consistency constraints," European Journal of Operational Research, Elsevier, vol. 274(1), pages 340-356.
    20. Gambardella, L.M. & Montemanni, R. & Weyland, D., 2012. "Coupling ant colony systems with strong local searches," European Journal of Operational Research, Elsevier, vol. 220(3), pages 831-843.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2193-:d:459438. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.