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.
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Acknowledgements
**aochen Chou was supported by the Swiss National Science Foundation through grant 200020\(\_\)156259: “Hybrid Sampling-based metaheuristics for Stochastic Optimization Problems with Deadlines”.
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Chou, X., Gambardella, L.M., Montemanni, R. (2018). Monte Carlo Sampling for the Probabilistic Orienteering Problem. In: Daniele, P., Scrimali, L. (eds) New Trends in Emerging Complex Real Life Problems. AIRO Springer Series, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-00473-6_19
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DOI: https://doi.org/10.1007/978-3-030-00473-6_19
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