A multi-objective imperialist competitive algorithm for vehicle routing problem in crossdocking networks with time windows

This study addresses the pickup and delivery problem for cross-docking strategy, in which shipments from suppliers to retailers are directly as well as through cross-docks. usual models that investigate vehicle routing in crossdocking networks force all vehicles to stop at the cross-dock even if a shipment is about to a full truckload or the vehicle collects and delivers the same set of products. in order to eliminate unnecessary stops at the dock, and thus reduce transportation costs, the designed model tries to decide about the best approach to deliver orders to retailers in a tailored network. in such a system, we have two objectives: minimization of the total transportation cost and minimization of the total earliness and tardiness of visiting retailers. in order to deal with this problem, we develop three multi-objective algorithms. an evolutionary algorithm based on multi objective imperialist competitive algorithm (moica) is proposed, and the associated results are compared with the results obtained by non-dominated sorting genetic algorithm (nsga-ii) and pareto archived evolution strategy (paes) in terms of some metrics. the computational results show the superiority of the proposed algorithm compared to other algorithms in some metrics.