IDEAS home Printed from https://ideas.repec.org/h/spr/oprchp/978-3-319-28697-6_62.html
   My bibliography  Save this book chapter

Sampling-Based Objective Function Evaluation Techniques for the Orienteering Problem with Stochastic Travel and Service Times

In: Operations Research Proceedings 2014

Author

Listed:
  • Vassilis Papapanagiotou

    (IDSIA-SUPSI-USI)

  • Roberto Montemanni

    (IDSIA-SUPSI-USI)

  • Luca Maria Gambardella

    (IDSIA-SUPSI-USI)

Abstract

StochasticPapapanagiotou, Vassilis CombinatorialMontemanni, Roberto Optimization ProblemsGambardella, Luca Maria are of great interest because they can model some quantities more accurately than their deterministic counterparts. However, the element of stochasticity introduces intricacies that make the objective function either difficult to evaluate or very time-consuming. In this paper, we propose and compare different sampling-based techniques for approximating the objective function for the Orienteering Problem with Stochastic Travel and Service Times.

Suggested Citation

  • Vassilis Papapanagiotou & Roberto Montemanni & Luca Maria Gambardella, 2016. "Sampling-Based Objective Function Evaluation Techniques for the Orienteering Problem with Stochastic Travel and Service Times," Operations Research Proceedings, in: Marco Lübbecke & Arie Koster & Peter Letmathe & Reinhard Madlener & Britta Peis & Grit Walther (ed.), Operations Research Proceedings 2014, edition 1, pages 445-450, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-28697-6_62
    DOI: 10.1007/978-3-319-28697-6_62
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bijun Wang & Zheyong Bian & Mo Mansouri, 2023. "Self-adaptive heuristic algorithms for the dynamic and stochastic orienteering problem in autonomous transportation system," Journal of Heuristics, Springer, vol. 29(1), pages 77-137, February.
    2. 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.

    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:oprchp:978-3-319-28697-6_62. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.