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Using Statistical Tests for Improving State-of-the-Art Heuristics for the Probabilistic Traveling Salesman Problem with Deadlines

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Computer Aided Systems Theory – EUROCAST 2011 (EUROCAST 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6927))

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Abstract

The Probabilistic Traveling Salesman Problem with Deadlines (PTSPD) is a Stochastic Vehicle Routing Problem with a computationally demanding objective function. Currently heuristics using an approximation of the objective function based on Monte Carlo Sampling are the state-of-the-art methods for the PTSPD. We show that those heuristics can be significantly improved by using statistical tests in combination with the sampling-based evaluation of solutions for the pairwise comparison of solutions.

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Author information

Authors and Affiliations

  1. IDSIA, Istituto Dalle Molle di Studi sull’Intelligenza Artificiale, Switzerland

    Dennis Weyland, Roberto Montemanni & Luca Maria Gambardella

Authors
  1. Dennis Weyland
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  2. Roberto Montemanni
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  3. Luca Maria Gambardella
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Editor information

Editors and Affiliations

  1. Instituto Universitario de Ciencias y Tecnologías Cibernéticas, Universidad de Las Palmas de Gran Canaria, Campus de Tafira, 35017, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz  & Alexis Quesada-Arencibia  & 

  2. Institute of Systems Science, Johannes Kepler University Linz, Altenbergerstrasse 69, 4040, Linz, Austria

    Franz Pichler

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© 2012 Springer-Verlag Berlin Heidelberg

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Weyland, D., Montemanni, R., Gambardella, L.M. (2012). Using Statistical Tests for Improving State-of-the-Art Heuristics for the Probabilistic Traveling Salesman Problem with Deadlines. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_57

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  • DOI: https://doi.org/10.1007/978-3-642-27549-4_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27548-7

  • Online ISBN: 978-3-642-27549-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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