Tackling Uncertainty in Online Multimodal Transportation Planning Using Deep Reinforcement Learning

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review


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%.
Originele taal-2Engels
TitelComputational Logistics - 12th International Conference, ICCL 2021, Proceedings
Subtitel12th International Conference, ICCL 2021, Enschede, The Netherlands, September 27–29, 2021, Proceedings
RedacteurenMartijn Mes, Eduardo Lalla-Ruiz, Stefan Voß
Plaats van productieCham
Aantal pagina's16
ISBN van elektronische versie978-3-030-87672-2
ISBN van geprinte versie978-3-030-87671-5
StatusGepubliceerd - 22 sep 2021
Evenement12th International Conference on Computational Logistics, ICCL 2021 - University of Twente, Enschede, Nederland
Duur: 27 sep 202129 sep 2021
Congresnummer: 12

Publicatie series

NaamLecture Notes in Computer Science (LNCS)
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349


Congres12th International Conference on Computational Logistics, ICCL 2021
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