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A study on dynamic scheduling of AGV in ACTs with consideration of battery constraints

Ruihua Wang et al.

Transportation Letters2026https://doi.org/10.1080/19427867.2026.2613148article
ABDC B
Weight
0.50

What the paper says

Automated guided vehicles (AGVs) have significantly improved automation and efficiency in automated container terminals (ACTs). As AGVs rely on electric power, their operational range directly influences their overall performance, making effective battery management essential. This study introduces the first application of wireless AGV charging at ports, developing a hybrid AGV charging system. A dual-engine mechanism is proposed: a rule engine formulates task assignment strategies, while a learning engine employing a hybrid metaheuristic-based algorithm optimizes routing. Integrating real-time battery status into scheduling achieves dynamic coordination between task allocation and path planning. Results show wireless charging reduces centralized charging events and shortens AGV idle time by 7.21%. Combined with optimized scheduling, terminal efficiency further improves, reducing operational time by an additional 10.31%. Optimal performance occurs at a 16% charging threshold, balancing charging frequency, equipment lifespan, and scheduling stability. These findings support effective decision-making for AGV energy strategies in ACTs.

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https://doi.org/https://doi.org/10.1080/19427867.2026.2613148

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@article{ruihua2026,
  title        = {{A study on dynamic scheduling of AGV in ACTs with consideration of battery constraints}},
  author       = {Ruihua Wang et al.},
  journal      = {Transportation Letters},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/19427867.2026.2613148},
}

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A study on dynamic scheduling of AGV in ACTs with consideration of battery constraints

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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