When barges are scheduled before the demand for container transport is known, the scheduled departures may match poorly with the realised demands’ due dates and with the truck utilization. Synchromodal transport enables simultaneous planning of container, truck and barge routes at the operational level. Often these decisions are taken by multiple stakeholders who wants cooperation, but are reluctant to share information. We propose a novel co-planning framework, called departure learning, where a barge operator learns what departure times perform better based on indications from the other operator. The framework is suitable for real time implementation and thus handles uncertainties by replanning. Simulated experiment results show that co-planning has a big impact on vehicle utilization and that departure learning is a promising tool for co-planning.
|Title of host publication||Computational Logistics|
|Subtitle of host publication||Proceedings of the 11th International Conference, ICCL 2020|
|Editors||Eduardo Lalla-Ruiz, Martijn Mes, Stefan Voß|
|Place of Publication||Cham, Switzerland|
|Publication status||Published - 2020|
|Event||11th International Conference on Computational Logistics, ICCL 2020 - Enschede, Netherlands|
Duration: 28 Sep 2020 → 30 Sep 2020
|Name||Lecture Notes in Computer Science|
|Conference||11th International Conference on Computational Logistics, ICCL 2020|
|Period||28/09/20 → 30/09/20|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- Cooperative planning
- Synchromodal transport
- Vehicle utilization