Tighter MIP models for Barge Container Ship Routing. (January 2019) - Alfandari, Laurent; Davidović, Tatjana; Furini, Fabio; Ljubić, Ivana; Maraš, Vladislav; Martin, Sébastien
Highlights: We propose new MIP formulations for Barge Container Ship Routing (liner ship). Both formulations use node variables associated with ports for routing decisions. The second model uses aggregated node variables instead of arc variables for empty containers. We also optimize the turnaround time and size of the fleet. Models provide tight dual bounds and significantly outperform existing models on benchmark instances. Abstract: This paper addresses the problem of optimal planning of a liner service for a barge container shipping company. Given estimated weekly demands between pairs of ports, our goal is to determine the subset of ports to be called and the amount of containers to be shipped between each pair of ports, so as to maximize the profit of the shipping company. In order to save possible leasing or storage costs of empty containers at the respective ports, our approach takes into account the repositioning of empty containers. The line has to follow the outbound–inbound principle, starting from the port at the river mouth. We propose a novel integrated approach in which the shipping company can simultaneously optimize the route (along with repositioning of empty containers), the choice of the final port, length of the turnaround time and the size of its fleet. To solve this problem, a new mixed integer programming model is proposed. On the publicly available set of benchmark instances for barge container routing, we demonstrate that this model provides very tight dual bounds and significantly outperforms the existing approaches from the literature for splittable demands. We also show how to further improve this model by projecting out arc variables for modeling the shipping of empty containers. Our numerical study indicates that the latter model improves the computing times for the challenging case of unsplittable demands. We also study the impact of the turnaround time optimization on the total profit of the company.