A Tabu Search algorithm for solving chance-constrained programs

Solution of real world problems have to deal with some uncertainties. This is particularly true for the planning of services whose requests are unknown a priori. Several approaches for solving stochastic problems are reported in the literature. Metaheuristics seem to be a powerful tool for computing good and robust solutions. However, the efficiency of algorithms based on Local Search, such as Tabu Search, suffers from the complexity of evaluating the objective function after each move. In this paper, we propose a Tabu Search algorithm which exploits simulation approach to solve chance-constrained programs. We prove its efficiency reporting the results of extensive computational experiments.