A methodology to solve large-scale cooperative transportation planning problems

In this paper, we suggest a methodology to solve a cooperative transportation planning problem and to assess its performance. The problem is motivated by a real-world scenario found in the German food industry. Several manufacturers with same customers but complementary food products share their vehicle fleets to deliver their customers. After an appropriate decomposition of the entire problem into sub problems, we obtain a set of rich vehicle routing problems (VRPs) with time windows for the delivery of the orders, capacity constraints, maximum operating times for the vehicles, and outsourcing options. Each of the resulting sub problems is solved by a greedy heuristic that takes the distance of the locations of customers and the time window constraints into account. The greedy heuristic is improved by an appropriate Ant Colony System (ACS). The suggested heuristics to solve the problem are assessed within a dynamic and stochastic environment in a rolling horizon setting using discrete event simulation. We describe the used simulation infrastructure. The results of extensive simulation experiments based on randomly generated problem instances and scenarios are provided and discussed. We show that the cooperative setting outperforms the non-cooperative one.