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Monte Carlo Sampling for the Probabilistic Orienteering Problem

  • Xiaochen Chou
  • Luca Maria Gambardella
  • Roberto Montemanni
Chapter
Part of the AIRO Springer Series book series (AIROSS, volume 1)

Abstract

The Probabilistic Orienteering Problem is a variant of the orienteering problem where customers are available with a certain probability. Given a solution, the calculation of the objective function value is complex since there is no linear expression for the expected total cost. In this work we approximate the objective function value with a Monte Carlo Sampling technique and present a computational study about precision and speed of such a method. We show that the evaluation based on Monte Carlo Sampling is fast and suitable to be used inside heuristic solvers. Monte Carlo Sampling is also used as a decisional tool to heuristically understand how many of the customers of a tour can be effectively visited before the given deadline is incurred.

Keywords

Probabilistic Orienteering Problem Monte Carlo Sampling Heuristic algorithms 

Notes

Acknowledgements

Xiaochen Chou was supported by the Swiss National Science Foundation through grant 200020\(\_\)156259: “Hybrid Sampling-based metaheuristics for Stochastic Optimization Problems with Deadlines”.

References

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xiaochen Chou
    • 1
  • Luca Maria Gambardella
    • 1
  • Roberto Montemanni
    • 1
  1. 1.IDSIA - Dalle Molle Institute for Artificial Intelligence (USI-SUPSI)MannoSwitzerland

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