Container Rehandling at Maritime Container Terminals: A Literature Update

Caserta, Marco; Schwarze, Silvia; Voß, Stefan

doi:10.1007/978-3-030-39990-0_16

Cited by 11 publications

(6 citation statements)

References 88 publications

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“…Other studies on shuffling reduction have focused on container retrieval problem [18][19][20][21][22]. Petering and Hussein [19] introduced a new mathematical formulation of the block relocation problem with fewer decision variables, and developed a new look-ahead algorithm for addressing the problem.…”

Section: Container Retrieval Problemmentioning

confidence: 99%

“…In constraint (15), the stack configuration is initialized utilizing the parameter ỹ𝑖𝓁𝑘 , and the configuration status of adjacent intervals and adjacent stages can be updated by constraints ( 16) and (17), respectively. Constraint (18) indicates that a slot can only hold one order. Constraint (19) ensures that orders cannot be suspended.…”

Section: Mathematical Model For Sps-tls Problemmentioning

confidence: 99%

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Joint optimization of steel plate shuffling and truck loading sequencing based on deep reinforcement learning

Xu,

Wang,

Yuan

et al. 2024

Advanced Engineering Informatics

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Section: Container Retrieval Problemmentioning

confidence: 99%

Section: Mathematical Model For Sps-tls Problemmentioning

confidence: 99%

Joint optimization of steel plate shuffling and truck loading sequencing based on deep reinforcement learning

Xu,

Wang,

Yuan

et al. 2024

Advanced Engineering Informatics

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“…The BRP, alternatively referred to as the container relocation problem (CRP), exists ( 22 ). Historically, scholars have frequently used BRP to represent two-dimensional scenes.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Guides the Solution of Blocks Relocation Problem in Container Terminals

Zheng

2022

Transportation Research Record: Journal of the Transportation R

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The blocks relocation problem (BRP) is a well known and important combinatorial optimization problem, in which the initial storage state and retrieval priority of containers are known, and the containers should be picked up in the retrieval order with the goal of minimizing container relocations. This paper studied how machine-learning techniques guide the solution of this Non-deterministic Polynomial problem(NP-hard problem) NP-hard problem. Through our self-developed data generator, we generated initial state stacking matrices and extracted 22 influencing factors for container relocation operations. The supervised learning method and attribution technique were used to verify the relationship between significant container relocated influence features and the number of container relocations using the unrestricted BRP and restricted BRP models. We characterize the potential patterns in the data based on 22 container relocated influence features using four supervised learning models: random forest (RF), extra trees (ET), support vector machine (SVM), and logistic regression (LR). The experimental results demonstrate that RF has a classification accuracy rate of up to 94% on the restricted BRP model. The attribution technique identifies the most sensitive features to the number of container relocations. We organically integrate machine learning into the BRP problem and propose an interactive iterative framework that may provide a novel method for studying the BRP problem.

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“…Ge, Meng, Liu, Tang, & Zhao (2020) address the problem of stacking slabs in the steel industry and Maniezzo, Boschetti, & Gutjahr (2021) the problem of stacking boxes in a warehouse. As for the stacking problems in container terminals, which are the subject of this paper, a recent survey by Caserta, Schwarze, & Voß (2020) classifies them into three types: premarshalling, re-marshalling, and block relocation problem. The premarshalling problem is concerned with finding the shortest sequence of moves that sorts the containers within a bay according to a known retrieval sequence.…”

Section: Literature Reviewmentioning

confidence: 99%