Stochastic local search procedures for the probabilistic two-day vehicle routing problem

This study is motivated by the study of a real-world application on blood delivery. The Austrian Red Cross (ARC), a non-profit organization, is in charge of delivering blood to hospitals on their request. To reduce their operating costs through higher flexibility, the ARC is interested in changing policy providing two different types of service: a urgent service which delivers the blood within one day and the other, regular service, within two days. Obviously the two services come at different prices. We formalize this problem as a stochastic problem, with the objective to minimize the average long-run delivery costs, knowing the probabilities governing the requests of service. To solve real instances of our problem in a reasonable time, we propose three heuristic procedures whose core routine is an Ant Colony Optimization algorithm, which differ from each other by the rule implemented to select the regular blood orders to serve immediately. We compare the three heuristics on both a set of real data and on set of randomly generated synthetic data. Computational results show the viability of our approach