AGV charging scheduling with capacitated charging stations at automated ports

Transportation Research Part E Logistics and Transportation Review, Год журнала: 2025, Номер 197, С. 104080 - 104080

Опубликована: Март 23, 2025

Язык: Английский

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A Reinforcement Learning‐Based AGV Scheduling for Automated Container Terminals With Resilient Charging Strategies

IET Intelligent Transport Systems, Год журнала: 2025, Номер 19(1)

Опубликована: Янв. 1, 2025

ABSTRACT Automated guided vehicles (AGVs) serve as pivotal equipment for horizontal transportation in automated container terminals (ACTs), necessitating the optimization of AGV scheduling. The dynamic nature port operations introduces uncertainties energy consumption, while battery constraints pose significant operational challenges. However, limited research has integrated charging and discharging behaviors into operations. This study innovatively proposes an scheduling model that incorporates a resilient adaptive strategy, adjusting balance between vehicle completion tasks, enabling AGVs to complete fixed tasks shortest time. Differing from most existing primarily based on OR‐typed algorithms, this reinforcement learning‐based method. Finally, series numerical experiments, which is real large‐scale terminal Pearl River Delta (PRD) region Southern China, are conducted verify effectiveness efficiency algorithm. Some beneficial management insights obtained sensitivity analysis practitioners. Notably, paramount observation efficacy does not necessarily correlate positively with their number. Instead, it follows “U‐shaped” curve trend, indicating optimal range beyond performance diminishes.

Язык: Английский

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Clean Energy Self-Consistent Systems for Automated Guided Vehicle (AGV) Logistics Scheduling in Automated Ports

Sustainability, Год журнала: 2025, Номер 17(8), С. 3411 - 3411

Опубликована: Апрель 11, 2025

To enhance the logistics scheduling efficiency of automated guided vehicles (AGVs) in ports and achieve orderly charging battery swapping AGVs as well self-sufficient clean energy, this paper proposes an integrated optimization method. The method first utilizes graph theory to construct a theoretical model that includes AGVs, port road network, battery-swapping stations, order analyze optimal strategies. Subsequently, for multi-objective problems AGV swapping, fast solution based on immune algorithm is proposed, with time self-sufficiency rate energy constraint conditions. Finally, effectiveness proposed verified through simulation scenario. results show simulated scenario, after optimization, total operation significantly reduced. Compared cases only consider time, strategy, or wind solar output, average under strategy increased by 82.7%, 27.5%, 53.9%, respectively. In addition, weight increases, both driving upward trend are approximately linearly related. Within specified maximum actual can be flexibly coordinated, significant carbon reduction benefits.

Язык: Английский

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Solving many-objective reentrant hybrid flowshop scheduling problem considering uncertainty factors in thin-film transistor liquid crystal display

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 111117 - 111117

Опубликована: Апрель 1, 2025

Язык: Английский

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AGV Scheduling and Energy Consumption Optimization in Automated Container Terminals Based on Variable Neighborhood Search Algorithm

Journal of Marine Science and Engineering, Год журнала: 2025, Номер 13(4), С. 647 - 647

Опубликована: Март 24, 2025

Automated Guided Vehicles (AGVs) for automated container terminals are mainly used horizontal transportation at the forefront of terminal. They shoulder responsibility between quay cranes and yard cranes. Optimizing their scheduling can not only improve operational efficiency, but also help reduce energy consumption promote green development port. This article first constructs a mathematical model with goal minimizing total AGVs, considering impact different states AGVs on during operation. Secondly, by using variable neighborhood search algorithm, AGV allocation operation tasks is optimized, sequence adjusted to consumption. The algorithm introduces five types operators random operator usage order expand range avoid local optima. Finally, influence number speed discussed, optimization performance genetic compared through computational experiments. research results show that proposed in this paper have significant effect reducing good stability practical application potential.

Язык: Английский

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Joint scheduling of hybrid flow-shop with limited automatic guided vehicles: A hierarchical learning-based swarm optimizer

Computers & Industrial Engineering, Год журнала: 2024, Номер 198, С. 110686 - 110686

Опубликована: Ноя. 5, 2024

Язык: Английский

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