A Metaheuristic Algorithm for the Probabilistic Orienteering Problem

Abstract

The Probabilistic Orienteering Problem (POP) is a variant of the orienteering problem where customers are available with a certain probability. In a previous work, we approximated its objective function value by using a Monte Carlo Sampling method. A heuristic speed-up criterion is considered in the objective function evaluator. In this work we study systematically the impact of the heuristic speed-up criterion in terms of precision and speed on the Monte Carlo evaluator, as well as the performance of a POP solver we propose, based on the embedding of the Monte Carlo evaluator into a Random Restart Local Search metaheuristic algorithm.