This paper presents an optimization approach which integrates Monte Carlo simulation (MCS) within a heuristic algorithm in order to deal with a rich and real-life vehicle routing problem. A set of customers' orders must be delivered from different depots and using a heterogeneous fleet of vehicles. Also, since the capacity of the firm's depots is limited, some vehicles might need to be replenished using external tanks. The MCS component, which is based on the use of a skewed probability distribution, allows to transform a deterministic heuristic into a probabilistic procedure. The geometric distribution is used to guide the local search process during the generation of high-quality solutions. The efficiency of our approach is tested against a real-world instance. The results show that our algorithm is capable of providing noticeable savings in short computing times.
['Gabriel Alemany', 'Jesica de Armas', 'Angel A. Juan', 'Álvaro García-Sánchez', 'Roberto García-Meizoso', 'Miguel Ortega-Mier']
Combining Monte Carlo simulation with heuristics to solve a rich and real-life multi-depot vehicle routing problem