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Uncertainty in Real-World Vehicle Routing (Extended Abstract)

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F25%3A00141880" target="_blank" >RIV/00216224:14330/25:00141880 - isvavai.cz</a>

  • Result on the web

    <a href="https://ojs.aaai.org/index.php/SOCS/article/view/36013" target="_blank" >https://ojs.aaai.org/index.php/SOCS/article/view/36013</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1609/socs.v18i1.36013" target="_blank" >10.1609/socs.v18i1.36013</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Uncertainty in Real-World Vehicle Routing (Extended Abstract)

  • Original language description

    The motivation for our research arises from the limitations of traditional deterministic heuristic solvers for vehicle routing problems (VRP) observed in industrial practice. In general, the quantities provided to the solvers as inputs, e.g., loads or service times, are typically estimates or simplifying reflections of reality. While current state-of-the-art solvers are applicable to complex VRP variants at scale, their inability to reason about uncertainties limits their usefulness in real-world applications. Despite stochastic VRPs being a widely studied topic, related approaches are typically centered around the uncertainty in the problem rather than extending successful deterministic methods. Moreover, uncertainty-related methodologies and models are often strongly linked to computationally expensive sampling or exact algorithms making their scaling problematic. Thus, we aim for easy-to-integrate, reusable, and especially computationally efficient mechanisms, allowing us to naturally extend state-of-the-art heuristic solvers for a wide range of VRPs with reasoning about input uncertainties. We formulate four mechanisms fitting these criteria, including standard chance constraints, two data manipulation methods, and a novel penalty-based method. These four mechanisms are compared and analyzed for the most common sources of uncertainty in loads and times on both benchmark and complex real-world instances. Their favorable scaling properties are demonstrated on instances with up to 1,000 customers.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10200 - Computer and information sciences

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2025

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    18th International Symposium on Combinatorial Search, SoCS 2025

  • ISBN

    9781577359012

  • ISSN

  • e-ISSN

  • Number of pages

    2

  • Pages from-to

    269-270

  • Publisher name

    AAAI Press

  • Place of publication

    Washington, DC, USA

  • Event location

    Glasgow, United Kingdom

  • Event date

    Jan 1, 2025

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article