We present a natural probabilistic variation of the multi-depot vehicle routing problem with pickup and delivery. We denote this variation by Stochastic multi-depot capacitated vehicle routing problem with pickup and delivery (SMCVRPPD). We present an algorithm to compute the expected length of an apriori route under general probabilistic assumptions. To solve the SMCVRPPD we propose an Iterated Local Search (ILS) and a Variable Neighborhood Search(VNS). We evaluate the performance of these heuristics on a data set adapted from TSPLIB instances. The results show that the ILS is effective to solve SMCVRPPD.
A hybrid adaptive Iterated Local Search with diversification control to the Capacitated Vehicle Routing Problem