Abstract
The Orienteering Problem is a routing problem aiming at selecting a subset of a given set of customers to be visited within a given time budget, so that a total revenue is maximized. Multiple variants of the problem have been studied. The Probabilistic Orienteering Problem is one of these variants, where customers will require a visit according to a certain given probability. Stochasticity makes the model more practical, but concurrently more difficult to solve. Tabu Search is a method widely used in combinatorial optimization problems to escape from local optima in heuristic local search procedures. In this work, we solve the Probabilistic Orienteering Problem by embedding a Monte Carlo evaluator into a Tabu Search algorithm, exploiting their interaction in an innovative way. A detailed computational study of the new approach is presented, with the aim of studying the performance of the metaheuristic algorithm in terms of precision and speed, while positioning the new method within the existing literature.
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