- https://arxiv.org/abs/1704.08470
Submitted manuscript

Research output: Working paper

Published

**An Experimental Comparison of Uncertainty Sets for Robust Shortest Path Problems.** / Dokka Venkata Satyanaraya, Trivikram; Goerigk, Marc.

Research output: Working paper

Dokka Venkata Satyanaraya, T & Goerigk, M 2017 'An Experimental Comparison of Uncertainty Sets for Robust Shortest Path Problems'. <https://arxiv.org/abs/1704.08470>

Dokka Venkata Satyanaraya, T., & Goerigk, M. (2017). *An Experimental Comparison of Uncertainty Sets for Robust Shortest Path Problems*. https://arxiv.org/abs/1704.08470

Dokka Venkata Satyanaraya T, Goerigk M. An Experimental Comparison of Uncertainty Sets for Robust Shortest Path Problems. 2017 Apr 27.

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title = "An Experimental Comparison of Uncertainty Sets for Robust Shortest Path Problems",

abstract = "Through the development of efficient algorithms, data structures and preprocessing techniques, real-world shortest path problems in street networks are now very fast to solve. But in reality, the exact travel times along each arc in the network may not be known. This lead to the development of robust shortest path problems, where all possible arc travel times are contained in a so-called uncertainty set of possible outcomes. Research in robust shortest path problems typically assumes this set to be given, and provides complexity results as well as algorithms depending on its shape. However, what can actually be observed in real-world problems are only discrete raw data points. The shape of the uncertainty is already a modelling assumption. In this paper we test several of the most widely used assumptions on the uncertainty set using real-world traffic measurements provided by the City of Chicago. We calculate the resulting different robust solutions, and evaluate which uncertainty approach is actually reasonable for our data. This anchors theoretical research in a real-world application and allows us to point out which robust models should be the future focus of algorithmic development.",

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AU - Dokka Venkata Satyanaraya, Trivikram

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N2 - Through the development of efficient algorithms, data structures and preprocessing techniques, real-world shortest path problems in street networks are now very fast to solve. But in reality, the exact travel times along each arc in the network may not be known. This lead to the development of robust shortest path problems, where all possible arc travel times are contained in a so-called uncertainty set of possible outcomes. Research in robust shortest path problems typically assumes this set to be given, and provides complexity results as well as algorithms depending on its shape. However, what can actually be observed in real-world problems are only discrete raw data points. The shape of the uncertainty is already a modelling assumption. In this paper we test several of the most widely used assumptions on the uncertainty set using real-world traffic measurements provided by the City of Chicago. We calculate the resulting different robust solutions, and evaluate which uncertainty approach is actually reasonable for our data. This anchors theoretical research in a real-world application and allows us to point out which robust models should be the future focus of algorithmic development.

AB - Through the development of efficient algorithms, data structures and preprocessing techniques, real-world shortest path problems in street networks are now very fast to solve. But in reality, the exact travel times along each arc in the network may not be known. This lead to the development of robust shortest path problems, where all possible arc travel times are contained in a so-called uncertainty set of possible outcomes. Research in robust shortest path problems typically assumes this set to be given, and provides complexity results as well as algorithms depending on its shape. However, what can actually be observed in real-world problems are only discrete raw data points. The shape of the uncertainty is already a modelling assumption. In this paper we test several of the most widely used assumptions on the uncertainty set using real-world traffic measurements provided by the City of Chicago. We calculate the resulting different robust solutions, and evaluate which uncertainty approach is actually reasonable for our data. This anchors theoretical research in a real-world application and allows us to point out which robust models should be the future focus of algorithmic development.

M3 - Working paper

BT - An Experimental Comparison of Uncertainty Sets for Robust Shortest Path Problems

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