2023
Li, Xiangda; Peng, Yun; Tian, Qi; Feng, Tao; Wang, Wenyuan; Cao, Zhen; Song, Xiangqun
In: Transportation Research Part E: Logistics and Transportation Review, vol. 180, pp. 103338, 2023, ISSN: 1366-5545.
Abstract | Links | BibTeX | Tags: Automated container terminal, Automated guided vehicles, Charging scheduling, Decomposition algorithm, Simulation-based optimization
@article{LI2023103338,
title = {A decomposition-based optimization method for integrated vehicle charging and operation scheduling in automated container terminals under fast charging technology},
author = {Xiangda Li and Yun Peng and Qi Tian and Tao Feng and Wenyuan Wang and Zhen Cao and Xiangqun Song},
url = {https://www.sciencedirect.com/science/article/pii/S1366554523003265},
doi = {https://doi.org/10.1016/j.tre.2023.103338},
issn = {1366-5545},
year = {2023},
date = {2023-01-01},
journal = {Transportation Research Part E: Logistics and Transportation Review},
volume = {180},
pages = {103338},
abstract = {The increasing utilization of battery-powered automated guided vehicles in automated container terminals, has an important consequence on terminal cost and efficiency. How to tackle integrated vehicle charging and operation scheduling problem to maintain high terminal performance is prominent for sustainable port operation. In this paper, fast charging technology is investigated, and a mixed integer programming model for this complicated scheduling problem is constructed, which aims to reduce charging cost and penalty cost related to makespan, and includes sequence-related constraints, time-related constraints and energy-related constraints. A decomposition-iteration algorithm is proposed to solve this problem, and furthermore it is combined with a simulation-based optimization method to address practical-sized instances. Numerical experiments on real-world cases are conducted to verify the efficiency and effectiveness of the proposed solution algorithm. Insightful managerial implications are derived by comparative analysis on charging rules and charging facility locations, and sensitivity analysis on charging power, charging facility configuration and vehicle configuration. Experimental results provide valuable references for terminal managers to make configuration and scheduling decisions for battery-powered vehicle transporting systems.},
keywords = {Automated container terminal, Automated guided vehicles, Charging scheduling, Decomposition algorithm, Simulation-based optimization},
pubstate = {published},
tppubtype = {article}
}
The increasing utilization of battery-powered automated guided vehicles in automated container terminals, has an important consequence on terminal cost and efficiency. How to tackle integrated vehicle charging and operation scheduling problem to maintain high terminal performance is prominent for sustainable port operation. In this paper, fast charging technology is investigated, and a mixed integer programming model for this complicated scheduling problem is constructed, which aims to reduce charging cost and penalty cost related to makespan, and includes sequence-related constraints, time-related constraints and energy-related constraints. A decomposition-iteration algorithm is proposed to solve this problem, and furthermore it is combined with a simulation-based optimization method to address practical-sized instances. Numerical experiments on real-world cases are conducted to verify the efficiency and effectiveness of the proposed solution algorithm. Insightful managerial implications are derived by comparative analysis on charging rules and charging facility locations, and sensitivity analysis on charging power, charging facility configuration and vehicle configuration. Experimental results provide valuable references for terminal managers to make configuration and scheduling decisions for battery-powered vehicle transporting systems.