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Yang, Y., Sun, S., Wu, Y., Feng, J., Lu, W., Wu, L....Postolache, O. (2025). Integrating multi-equipment scheduling with accurate AGV path planning for U-shaped automated container terminals. Computers and Industrial Engineering. 209

Y. Yang et al.,  "Integrating multi-equipment scheduling with accurate AGV path planning for U-shaped automated container terminals", in Computers and Industrial Engineering, vol. 209, 2025

@article{yang2025_1777281563832,
	author = "Yang, Y. and Sun, S. and Wu, Y. and Feng, J. and Lu, W. and Wu, L. and Postolache, O.",
	title = "Integrating multi-equipment scheduling with accurate AGV path planning for U-shaped automated container terminals",
	journal = "Computers and Industrial Engineering",
	year = "2025",
	volume = "209",
	number = "",
	doi = "10.1016/j.cie.2025.111427",
	url = "https://www.sciencedirect.com/journal/computers-and-industrial-engineering"
}

TY  - JOUR
TI  - Integrating multi-equipment scheduling with accurate AGV path planning for U-shaped automated container terminals
T2  - Computers and Industrial Engineering
VL  - 209
AU  - Yang, Y.
AU  - Sun, S.
AU  - Wu, Y.
AU  - Feng, J.
AU  - Lu, W.
AU  - Wu, L.
AU  - Postolache, O.
PY  - 2025
SN  - 0360-8352
DO  - 10.1016/j.cie.2025.111427
UR  - https://www.sciencedirect.com/journal/computers-and-industrial-engineering
AB  - In the context of the continuous growth of global container transport demand, people are paying more and more attention to the operational efficiency and energy consumption of automated container terminals (ACTs). This study focuses on the scheduling and path planning of Automated Guided Vehicles (AGVs) in complex environments. It aims to address the challenges that arise from direct interaction of multiple equipment in U-shaped ACTs. In this paper, a multi-equipment cooperative scheduling method based on AGV accurate path planning is proposed for the first time, aiming to minimize the total energy consumption of all equipment. Specifically, we establish a two-layer mathematical model for multi-equipment collaborative scheduling and AGV path planning, considering turning and lane-changing. Then, an Adaptive Genetic Algorithm based on the Jaya strategy and an Accurate Path Planning Algorithm are designed to solve this model. Numerical experiments show that the proposed method can significantly improve the calculation speed and reduce the number of path nodes passed by the AGV. This study provides strong support for terminal managers’ equipment scheduling strategy and energy consumption optimization strategy.
ER  -