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<p>In automated container terminals (ACTs), battery-powered automated guided vehicles (AGVs) play a vital role in promoting green and low-carbon operations. Although the AGV scheduling problem has been extensively studied, existing research typically overlooks the limitations of AGV battery capacity and charging station availability, which present significant challenges for practical applications. To address the AGV charging scheduling problem, this study proposes a novel multi-stage threshold opportunistic charging strategy to overcome these issues. The strategy sets multi-stage thresholds and utilizes multiple charging control methods to determine when, where and how much each AGV should be recharged. A mixed-integer programming model is formulated to integrate sequence, time, and battery constraints, aiming to minimize the total energy consumption of AGVs. Additionally, a position-based heuristic adaptive large neighborhood search algorithm is developed to solve this problem. A position-based heuristic method is used for initialization to generate high-quality initial solutions. The algorithm uses a matrix encoding scheme, and an individual right-shifted approach is proposed for sequence decoding. Through numerical experiments based on real-world cases, the proposed charging strategy and algorithm demonstrate improved operational efficiency and reduced total energy consumption in the AGV system. The management recommendations derived from the sensitivity analysis provide crucial support for the sustainable development of ACTs, assisting operators to make more accurate operational decisions in the complex terminal environment.</p>

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An integrated scheduling method for AGVs based on a multi-stage threshold charging strategy in automated container terminals

  • 期刊论文

  • 发表日期：

    2026年1月10日

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  • Qiang Zhang,
  • Fang Yu,
  • Suosuo Huang,
  • Yongsheng Yang

摘要

In automated container terminals (ACTs), battery-powered automated guided vehicles (AGVs) play a vital role in promoting green and low-carbon operations. Although the AGV scheduling problem has been extensively studied, existing research typically overlooks the limitations of AGV battery capacity and charging station availability, which present significant challenges for practical applications. To address the AGV charging scheduling problem, this study proposes a novel multi-stage threshold opportunistic charging strategy to overcome these issues. The strategy sets multi-stage thresholds and utilizes multiple charging control methods to determine when, where and how much each AGV should be recharged. A mixed-integer programming model is formulated to integrate sequence, time, and battery constraints, aiming to minimize the total energy consumption of AGVs. Additionally, a position-based heuristic adaptive large neighborhood search algorithm is developed to solve this problem. A position-based heuristic method is used for initialization to generate high-quality initial solutions. The algorithm uses a matrix encoding scheme, and an individual right-shifted approach is proposed for sequence decoding. Through numerical experiments based on real-world cases, the proposed charging strategy and algorithm demonstrate improved operational efficiency and reduced total energy consumption in the AGV system. The management recommendations derived from the sensitivity analysis provide crucial support for the sustainable development of ACTs, assisting operators to make more accurate operational decisions in the complex terminal environment.