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Title:Learning-based co-planning for improved container, barge and truck routing
Authors:R.B. Larsen, B. Atasoy, R.R. Negenborn

Conference:11th International Conference on Computational Logistics (ICCL'20)
Address:Enschede, The Netherlands
Date:September 2020

Abstract: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 shows that co-planning has a big impact on vehicle utilization and that departure learning is a promising tool for co-planning.

Reference:R.B. Larsen, B. Atasoy, R.R. Negenborn. Learning-based co-planning for improved container, barge and truck routing. In Proceedings of the 11th International Conference on Computational Logistics (ICCL'20), Enschede, The Netherlands, pp. 476-491, September 2020.
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