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 language | English |
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Title of host publication | Computational Logistics - 12th International Conference, ICCL 2021, Proceedings |
Subtitle of host publication | 12th International Conference, ICCL 2021, Enschede, The Netherlands, September 27–29, 2021, Proceedings |
Editors | Martijn Mes, Eduardo Lalla-Ruiz, Stefan Voß |
Place of Publication | Cham |
Publisher | Springer |
Chapter | 38 |
Pages | 578-593 |
Number of pages | 16 |
ISBN (Electronic) | 978-3-030-87672-2 |
ISBN (Print) | 978-3-030-87671-5 |
DOIs | |
Publication status | Published - 22 Sept 2021 |
Event | 12th International Conference on Computational Logistics, ICCL 2021 - University of Twente, Enschede, Netherlands Duration: 27 Sept 2021 → 29 Sept 2021 Conference number: 12 https://iccl2021.nl/ |
Publication series
Name | Lecture Notes in Computer Science (LNCS) |
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Publisher | Springer |
Volume | 13004 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 12th International Conference on Computational Logistics, ICCL 2021 |
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Country/Territory | Netherlands |
City | Enschede |
Period | 27/09/21 → 29/09/21 |
Internet address |
Funding
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).
Funders | Funder number |
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European Commission | INEA/CEF/TRAN/M2018/1793401 |
Keywords
- Optimization
- Deep Reinforcement Learning
- Online planning under uncertainty
- Multimodal transport