Tackling Uncertainty in Online Multimodal Transportation Planning Using Deep Reinforcement Learning

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In this paper we tackle the container allocation problem in multimodal transportation planning under uncertainty in container arrival times, using Deep Reinforcement Learning. The proposed approach can take real-time decisions on allocating individual containers to a truck or to trains, while a transportation plan is being executed. We evaluated our method using data that reflect a realistic scenario, designed on the basis of a case study at a logistics company with three different uncertainty levels based on the probability of delays in container arrivals. The experiments show that Deep Reinforcement Learning methods outperform heuristics, a stochastic programming method, and methods that use periodic re-planning, in terms of total transportation costs at all levels of uncertainty, obtaining an average cost difference with the optimal solution within 0.37% and 0.63%.
Original languageEnglish
Title of host publicationComputational Logistics - 12th International Conference, ICCL 2021, Proceedings
Subtitle of host publication12th International Conference, ICCL 2021, Enschede, The Netherlands, September 27–29, 2021, Proceedings
EditorsMartijn Mes, Eduardo Lalla-Ruiz, Stefan Voß
Place of PublicationCham
Number of pages16
ISBN (Electronic)978-3-030-87672-2
ISBN (Print)978-3-030-87671-5
Publication statusPublished - 22 Sept 2021
Event12th International Conference on Computational Logistics, ICCL 2021 - University of Twente, Enschede, Netherlands
Duration: 27 Sept 202129 Sept 2021
Conference number: 12

Publication series

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


Conference12th International Conference on Computational Logistics, ICCL 2021
Internet address


The work leading up to this paper is partly funded by the European Commission under the FENIX project (grant nr. INEA/CEF/TRAN/M2018/1793401).

FundersFunder number
European CommissionINEA/CEF/TRAN/M2018/1793401


    • Optimization
    • Deep Reinforcement Learning
    • Online planning under uncertainty
    • Multimodal transport


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