Simulation details underlying the publication: A learning-based co-planning method with truck and container routing for improved barge departure times
doi:10.4121/3c3bf8b0-c296-450b-8dce-6bfa4d1ad63c.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future.
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doi: 10.4121/3c3bf8b0-c296-450b-8dce-6bfa4d1ad63c
doi: 10.4121/3c3bf8b0-c296-450b-8dce-6bfa4d1ad63c
Datacite citation style:
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
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
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.
history
- 2023-12-18 first online, published, posted
publisher
4TU.ResearchData
format
Matlab .mat, .m
associated peer-reviewed publication
A learning-based co-planningmethod with truck and container routingfor improved barge departure times
funding
- 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
organizations
TU Delft, Faculty of Mechanical, Maritime and Materials Engineering (3ME), Department of Maritime and Transport Technology
DATA
files (8)
- 7,730 bytesMD5:
64e45b6c3458699b5083c1fe4ff284cd
createABD.m - 1,445 bytesMD5:
0eeb2604a742f70cf37c43e03aa25c32
Demand_profile_Peaks.mat - 1,388 bytesMD5:
7df26c4f886d7de08822733c8032889c
Demand_profile_Peaks2.mat - 1,415 bytesMD5:
14f0765181a493504b22a6f65e206c58
Demand_profile_Peaks4.mat - 1,457 bytesMD5:
f307aec317b42e3aa423f91fe325c86d
Demand_profile_Peaks6.mat - 1,359 bytesMD5:
d952613cb277326585c249e8ef588607
Demand_profile_Unbalanced.mat - 1,589 bytesMD5:
a0ed7395423fd6cafc9fe9ca5c3b26a8
Demand_profile_Unbalanced6.mat - 1,234 bytesMD5:
2b73bacaeabaf1e27a2b7ff992530041
RealisticScenario_vehicleCentered.m -
download all files (zip)
17,617 bytes unzipped