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.

Monte Carlo Sampling for the Probabilistic Orienteering Problem / Chou, Xiaochen; Gambardella Luca, Maria; Montemanni, Roberto. - (2018), pp. 169-177. ((Intervento presentato al convegno ODS 2018 tenutosi a Taormina, Italy nel September 2018.

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