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

Chassein, Andre and Dokka Venkata Satyanaraya, Trivikram and Goerigk, Marc (2019) Algorithms and uncertainty sets for data-driven robust shortest path problems. European Journal of Operational Research, 274 (2). pp. 671-686. ISSN 0377-2217

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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.

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Journal Article
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European Journal of Operational Research
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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
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25 Oct 2018 15:06
Last Modified:
11 May 2022 06:10