The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/3c3bf8b0-c296-450b-8dce-6bfa4d1ad63c

Larsen, Rie; Negenborn, R.R. (Rudy); Atasoy, Bilge (2023): Simulation details underlying the publication: A learning-based co-planning method with truck and container routing for improved barge departure times. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/3c3bf8b0-c296-450b-8dce-6bfa4d1ad63c.v1

Dataset

• Water Transport
• Environmentally Sustainable Transport
• Ground Transport
• Other Transport
• Transport

Barge scheduling, Co-planning, Cooperative planning, Integrated Planning, Intermodal transport, Synchromodal transport, Vehicle utilization

CC BY-NC-SA 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Rie Larsen , R.R. (Rudy) Negenborn , Bilge Atasoy

Demand profiles

Network details (including costs)

.m file for generating the 3-node truck-system in a standard x(t+1)=Ax(t)+Bu(t)+Dv(t) format

All created for and used in the Annals of Operations Research article A learning-based co-planningmethod with truck and container routingfor improved barge departure times.

• 2023-12-18 first online, published, posted

4TU.ResearchData

Matlab .mat, .m

A learning-based co-planningmethod with truck and container routingfor improved barge departure times

• Complexity Methods for Predictive Synchromodality (Comet-PS) (grant code 439.16.120) [more info...] Dutch Research Council
• Novel inland waterway transport concepts for moving freight effectively (grant code 858508) [more info...] European Commission

TU Delft, Faculty of Mechanical, Maritime and Materials Engineering (3ME), Department of Maritime and Transport Technology