Tackling Uncertainty in Online Multimodal Transportation Planning Using Deep Reinforcement Learning

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Abstract

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
PublisherSpringer, Cham
Chapter38
Pages578-593
Number of pages16
ISBN (Electronic)978-3-030-87672-2
ISBN (Print)978-3-030-87671-5
DOIs
Publication statusPublished - 22 Sep 2021
Event12th International Conference on Computational Logistics, ICCL 2021 - University of Twente, Enschede, Netherlands
Duration: 27 Sep 202129 Sep 2021
Conference number: 12
https://iccl2021.nl/

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer
Volume13004
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Computational Logistics, ICCL 2021
Country/TerritoryNetherlands
CityEnschede
Period27/09/2129/09/21
Internet address

Keywords

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

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