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    Rights statement: This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 274, 2, 2018 DOI: 10.1016/j.ejor.2018.10.006

    Accepted author manuscript, 1.14 MB, PDF document

    Embargo ends: 11/10/20

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Algorithms and uncertainty sets for data-driven robust shortest path problems

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>16/04/2019
<mark>Journal</mark>European Journal of Operational Research
Issue number2
Volume274
Number of pages16
Pages (from-to)671-686
Publication statusPublished
Early online date11/10/18
Original languageEnglish

Abstract

We consider robust shortest path problems, where the aim is to find a path that optimizes the worst-case performance over an uncertainty set containing all relevant scenarios for arc costs. The usual approach for such problems is to assume this uncertainty set given by an expert who can advise on the shape and size of the set.

Following the idea of data-driven robust optimization, we instead construct a range of uncertainty sets from the current literature based on real-world traffic measurements provided by the City of Chicago. We then compare the performance of the resulting robust paths within and outside the sample, which allows us to draw conclusions on the suitability of uncertainty sets.

Based on our experiments, we then focus on ellipsoidal uncertainty sets, and develop a new solution algorithm that significantly outperforms a state-of-the art solver.

Bibliographic note

This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 274, 2, 2018 DOI: 10.1016/j.ejor.2018.10.006