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Wei L, Wei F, Schmitz S, Kunal K (2021). Optimization of Container Relocation Problem via Reinforcement Learning. Logistics Journal : Proceedings, Vol. 2021. (urn:nbn:de:0009-14-54466)

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%0 Journal Article
%T Optimization of Container Relocation Problem via Reinforcement Learning
%A Wei, Lei
%A Wei, Fuyin
%A Schmitz, Sandra
%A Kunal, Kunal
%J Logistics Journal : Proceedings
%D 2021
%V 2021
%N 17
%@ 2192-9084
%F wei2021
%X This paper presents an optimization method of Container Relocation Problem (CRP) via Reinforcement Learning (RL) based on heuristic rules. The method to calculate theoretical lowest relocation rate is also briefly explained. As the result, training models for different dimensional bays are provided. Compared to the theoretical value, the result relocation rate is acceptable with high inference speed. Furthermore, extended CRP in block will be briefly demonstrated.
%L 620
%K Block Relocation Problem
%K Container Relocation Problem
%K ML-Agents
%K Reinforcement Learning
%R 10.2195/lj_Proc_wei_en_202112_02
%U http://nbn-resolving.de/urn:nbn:de:0009-14-54466
%U http://dx.doi.org/10.2195/lj_Proc_wei_en_202112_02
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@Article{wei2021,
  author = 	"Wei, Lei
		and Wei, Fuyin
		and Schmitz, Sandra
		and Kunal, Kunal",
  title = 	"Optimization of Container Relocation Problem via Reinforcement Learning",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2021",
  volume = 	"2021",
  number = 	"17",
  keywords = 	"Block Relocation Problem; Container Relocation Problem; ML-Agents; Reinforcement Learning",
  abstract = 	"This paper presents an optimization method of Container Relocation Problem (CRP) via Reinforcement Learning (RL) based on heuristic rules. The method to calculate theoretical lowest relocation rate is also briefly explained. As the result, training models for different dimensional bays are provided. Compared to the theoretical value, the result relocation rate is acceptable with high inference speed. Furthermore, extended CRP in block will be briefly demonstrated.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_Proc_wei_en_202112_02",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-54466"
}
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RIS

TY  - JOUR
AU  - Wei, Lei
AU  - Wei, Fuyin
AU  - Schmitz, Sandra
AU  - Kunal, Kunal
PY  - 2021
DA  - 2021//
TI  - Optimization of Container Relocation Problem via Reinforcement Learning
JO  - Logistics Journal : Proceedings
VL  - 2021
IS  - 17
KW  - Block Relocation Problem
KW  - Container Relocation Problem
KW  - ML-Agents
KW  - Reinforcement Learning
AB  - This paper presents an optimization method of Container Relocation Problem (CRP) via Reinforcement Learning (RL) based on heuristic rules. The method to calculate theoretical lowest relocation rate is also briefly explained. As the result, training models for different dimensional bays are provided. Compared to the theoretical value, the result relocation rate is acceptable with high inference speed. Furthermore, extended CRP in block will be briefly demonstrated.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-54466
DO  - 10.2195/lj_Proc_wei_en_202112_02
ID  - wei2021
ER  -
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ISI

PT Journal
AU Wei, L
   Wei, F
   Schmitz, S
   Kunal, K
TI Optimization of Container Relocation Problem via Reinforcement Learning
SO Logistics Journal : Proceedings
PY 2021
VL 2021
IS 17
DI 10.2195/lj_Proc_wei_en_202112_02
DE Block Relocation Problem; Container Relocation Problem; ML-Agents; Reinforcement Learning
AB This paper presents an optimization method of Container Relocation Problem (CRP) via Reinforcement Learning (RL) based on heuristic rules. The method to calculate theoretical lowest relocation rate is also briefly explained. As the result, training models for different dimensional bays are provided. Compared to the theoretical value, the result relocation rate is acceptable with high inference speed. Furthermore, extended CRP in block will be briefly demonstrated.
ER
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Full Metadata

Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2021, Iss. 17
Title

Optimization of Container Relocation Problem via Reinforcement Learning (eng)
Author Lei Wei, Fuyin Wei, Sandra Schmitz, Kunal Kunal
Language eng
Abstract This paper presents an optimization method of Container Relocation Problem (CRP) via Reinforcement Learning (RL) based on heuristic rules. The method to calculate theoretical lowest relocation rate is also briefly explained. As the result, training models for different dimensional bays are provided. Compared to the theoretical value, the result relocation rate is acceptable with high inference speed. Furthermore, extended CRP in block will be briefly demonstrated. In dieser Arbeit wird eine Optimierungsmethode für das Container Relocation Problem (CRP) mittels Reinforcement Learning (RL) vorgestellt, die auf heuristischen Regeln basiert. Eine Methode zur Berechnung der theoretisch niedrigsten Relocation Rate wird ebenfalls erläutert. Als Ergebnis werden Trainingsmodelle für unterschiedlich dimensionierte Bays bereitgestellt. Verglichen mit dem theoretischen Wert, ist die Relocation Rate zufriedenstellend und die Inferenz-Geschwindigkeit hoch. Außerdem wird eine erweiterte Version des CRPs die sich auf einen ganzen Containerblock bezieht, präsentiert.
Subject Block Relocation Problem, Container Relocation Problem, ML-Agents, Reinforcement Learning
DDC 620
Rights fDPPL
URN: urn:nbn:de:0009-14-54466
DOI https://doi.org/10.2195/lj_Proc_wei_en_202112_02