An Ant Colony optimization approach to solve cooperative transportation planning problems

In this paper, we suggest efficient heuristics to solve a cooperative transportation planning problem that is motivated by a scenario found in the German food industry. After an appropriate decomposition of the entire problem into sub problems, we obtain a set of rich vehicle routing problems (VRP) including due dates for the delivery of the orders, capacity constraints and maximum operating time window constraints for the vehicles, and outsourcing options. Each of these 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 further improved by applying an Ant Colony System (ACS). The suggested heuristics are assessed in a rolling horizon setting using discrete event simulation. The results of some preliminary computational experiments are provided. We show that the ACS based heuristic outperforms the greedy heuristic.