Learning-Based Co-planning for Improved Container, Barge and Truck Routing

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


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.

Original languageEnglish
Title of host publicationComputational Logistics
Subtitle of host publicationProceedings of the 11th International Conference, ICCL 2020
EditorsEduardo Lalla-Ruiz, Martijn Mes, Stefan Voß
Place of PublicationCham, Switzerland
ISBN (Electronic)978-3-030-59747-4
ISBN (Print)978-3-030-59746-7
Publication statusPublished - 2020
Event11th International Conference on Computational Logistics, ICCL 2020 - Enschede, Netherlands
Duration: 28 Sep 202030 Sep 2020

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Computational Logistics, ICCL 2020


  • Cooperative planning
  • Synchromodal transport
  • Vehicle utilization

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