IDEAS home Printed from https://ideas.repec.org/a/spr/aqjoor/v16y2018i4d10.1007_s10288-018-0371-9.html
   My bibliography  Save this article

Generating constrained length personalized bicycle tours

Author

Listed:
  • P. Stroobant

    (Ghent University, imec, IDLab, iGent Tower)

  • P. Audenaert

    (Ghent University, imec, IDLab, iGent Tower)

  • D. Colle

    (Ghent University, imec, IDLab, iGent Tower)

  • M. Pickavet

    (Ghent University, imec, IDLab, iGent Tower)

Abstract

In the context of recreational routing, the problem of finding a route which starts and ends in the same location (while achieving a length between specified upper and lower boundaries) is a common task, especially for tourists or cyclists who want to exercise. The topic of finding a tour between a specified starting and ending location while minimizing one or multiple criteria is well covered in literature. In contrast to this, the route planning task in which a pleasant tour with length between a maximum and a minimum boundary needs to be found is relatively underexplored. In this paper, we provide a formal definition of this problem, taking into account the existing literature on which route attributes influence cyclists in their route choice. We show that the resulting problem is NP-hard and devise a branch-and-bound algorithm that is able to provide a bound on the quality of the best solution in pseudo-polynomial time. Furthermore, we also create an efficient heuristic to tackle the problem and we compare the quality of the solutions that are generated by the heuristic with the bounds provided by the branch-and-bound algorithm. Also, we thoroughly discuss the complexity and running time of the heuristic.

Suggested Citation

  • P. Stroobant & P. Audenaert & D. Colle & M. Pickavet, 2018. "Generating constrained length personalized bicycle tours," 4OR, Springer, vol. 16(4), pages 411-439, December.
    • Handle: RePEc:spr:aqjoor:v:16:y:2018:i:4:d:10.1007_s10288-018-0371-9
    DOI: 10.1007/s10288-018-0371-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10288-018-0371-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10288-018-0371-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Su, Jason G. & Winters, Meghan & Nunes, Melissa & Brauer, Michael, 2010. "Designing a route planner to facilitate and promote cycling in Metro Vancouver, Canada," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(7), pages 495-505, August.
    2. Loiola, Eliane Maria & de Abreu, Nair Maria Maia & Boaventura-Netto, Paulo Oswaldo & Hahn, Peter & Querido, Tania, 2007. "A survey for the quadratic assignment problem," European Journal of Operational Research, Elsevier, vol. 176(2), pages 657-690, January.
    3. E. L. Lawler & D. E. Wood, 1966. "Branch-and-Bound Methods: A Survey," Operations Research, INFORMS, vol. 14(4), pages 699-719, August.
    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. Pucher, John & Buehler, Ralph & Seinen, Mark, 2011. "Bicycling renaissance in North America? An update and re-appraisal of cycling trends and policies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(6), pages 451-475, July.
    2. Kezong Tang & Xiong-Fei Wei & Yuan-Hao Jiang & Zi-Wei Chen & Lihua Yang, 2023. "An Adaptive Ant Colony Optimization for Solving Large-Scale Traveling Salesman Problem," Mathematics, MDPI, vol. 11(21), pages 1-26, October.
    3. Anowar, Sabreena & Eluru, Naveen & Hatzopoulou, Marianne, 2017. "Quantifying the value of a clean ride: How far would you bicycle to avoid exposure to traffic-related air pollution?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 66-78.
    4. Yunpeng Sun & Ruoya Jia & Asif Razzaq & Qun Bao, 2023. "RETRACTED ARTICLE: Drivers of China’s geographical renewable energy development: evidence from spatial association network structure approaches," Economic Change and Restructuring, Springer, vol. 56(6), pages 4115-4163, December.
    5. Herrán, Alberto & Manuel Colmenar, J. & Duarte, Abraham, 2021. "An efficient variable neighborhood search for the Space-Free Multi-Row Facility Layout problem," European Journal of Operational Research, Elsevier, vol. 295(3), pages 893-907.
    6. Weiqiang Pan & Zhilong Shan & Ting Chen & Fangjiong Chen & Jing Feng, 2016. "Optimal pilot design for OFDM systems with non-contiguous subcarriers based on semi-definite programming," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 63(2), pages 297-305, October.
    7. Ricardo M. Lima & Ignacio E. Grossmann, 2017. "On the solution of nonconvex cardinality Boolean quadratic programming problems: a computational study," Computational Optimization and Applications, Springer, vol. 66(1), pages 1-37, January.
    8. Fox, B. L. & Lenstra, J. K. & Rinnooy Kan, A. H. G. & Schrage, L. E., 1977. "Branching From The Largest Upper Bound: Folklore And Facts," Econometric Institute Archives 272158, Erasmus University Rotterdam.
    9. Alfonsas Misevičius & Aleksandras Andrejevas & Armantas Ostreika & Dovilė Verenė & Gintarė Žekienė, 2024. "An Improved Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem," Mathematics, MDPI, vol. 12(23), pages 1-25, November.
    10. Michela Ricciardi Celsi & Lorenzo Ricciardi Celsi, 2024. "Quantum Computing as a Game Changer on the Path towards a Net-Zero Economy: A Review of the Main Challenges in the Energy Domain," Energies, MDPI, vol. 17(5), pages 1-22, February.
    11. Krešimir Mihić & Kevin Ryan & Alan Wood, 2018. "Randomized Decomposition Solver with the Quadratic Assignment Problem as a Case Study," INFORMS Journal on Computing, INFORMS, vol. 30(2), pages 295-308, May.
    12. Alistair Wilson & Mariagiovanna Baccara & Ayse Imrohoroglu & Leeat Yariv, 2009. "A Field Study on Matching with Network Externalities," Working Paper 486, Department of Economics, University of Pittsburgh, revised Sep 2011.
    13. Lin, Jen-Jia & Wei, Yi-Hsuan, 2018. "Assessing area-wide bikeability: A grey analytic network process," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 381-396.
    14. Jia, Zhao-hong & Li, Kai & Leung, Joseph Y.-T., 2015. "Effective heuristic for makespan minimization in parallel batch machines with non-identical capacities," International Journal of Production Economics, Elsevier, vol. 169(C), pages 1-10.
    15. Pessoa, Artur Alves & Hahn, Peter M. & Guignard, Monique & Zhu, Yi-Rong, 2010. "Algorithms for the generalized quadratic assignment problem combining Lagrangean decomposition and the Reformulation-Linearization Technique," European Journal of Operational Research, Elsevier, vol. 206(1), pages 54-63, October.
    16. Notte, Gastón & Pedemonte, Martín & Cancela, Héctor & Chilibroste, Pablo, 2016. "Resource allocation in pastoral dairy production systems: Evaluating exact and genetic algorithms approaches," Agricultural Systems, Elsevier, vol. 148(C), pages 114-123.
    17. Angel Juan & Javier Faulin & Albert Ferrer & Helena Lourenço & Barry Barrios, 2013. "MIRHA: multi-start biased randomization of heuristics with adaptive local search for solving non-smooth routing problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 109-132, April.
    18. Chen, Jingxu & Li, Zhibin & Jiang, Hang & Zhu, Senlai & Wang, Wei, 2017. "Simulating the impacts of on-street vehicle parking on traffic operations on urban streets using cellular automation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 880-891.
    19. Huizhen Zhang & Cesar Beltran-Royo & Liang Ma, 2013. "Solving the quadratic assignment problem by means of general purpose mixed integer linear programming solvers," Annals of Operations Research, Springer, vol. 207(1), pages 261-278, August.
    20. Juan F. R. Herrera & José M. G. Salmerón & Eligius M. T. Hendrix & Rafael Asenjo & Leocadio G. Casado, 2017. "On parallel Branch and Bound frameworks for Global Optimization," Journal of Global Optimization, Springer, vol. 69(3), pages 547-560, November.

    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:spr:aqjoor:v:16:y:2018:i:4:d:10.1007_s10288-018-0371-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.