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<p>The airport ground cargo transportation is an important part of air cargo industry, and the effective scheduling of the resources involved in the airport ground cargo transportation is crucial in improving the quality and efficiency of the air logistics service. This study investigates the Airport Ground Cargo Transportation Scheduling (AGCTS) problem by considering the simultaneous scheduling of airport logistics vehicles and handling facilities under strict time restrictions. A mathematical model inspired by the flexible job shop scheduling problem (FJSP) with transportation is presented, aiming to minimize the total service duration time for flights, the total waiting time of logistics vehicles, and the imbalance of logistics vehicles usage. To solve AGCTS, an algorithm based on the elitist nondominated sorting genetic algorithm version II (NSGAII), named NSGAII-GRSr is developed. With a problem-based triple-layer encoding method and a state-based decoding operator, a bridge is built between the chromosome representation and the solution. To improve the algorithm performance, a Greedy-Random Selection (GRS) strategy is integrated in population initialization and a crossover operator rPOS is developed. Besides, a benchmark with 225 instances for AGCTS is designed, on which experimental study is conducted. The experimental results confirm the validity of the proposed multi-objective model and the superior performance of NSGAII-GRSr over the original NSGAII as well as five other well-known algorithms in solving AGCTS.</p>

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Airport ground cargo transportation scheduling problem: simultaneously considering logistics vehicles and handling facilities under strict time restriction

  • 期刊论文

  • 发表日期：

    2025年11月13日

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  • Ran Feng,
  • Wanru Gao,
  • Yafei Li,
  • Zhihao Cai,
  • Guoliang Chen,
  • Mingliang Xu

摘要

The airport ground cargo transportation is an important part of air cargo industry, and the effective scheduling of the resources involved in the airport ground cargo transportation is crucial in improving the quality and efficiency of the air logistics service. This study investigates the Airport Ground Cargo Transportation Scheduling (AGCTS) problem by considering the simultaneous scheduling of airport logistics vehicles and handling facilities under strict time restrictions. A mathematical model inspired by the flexible job shop scheduling problem (FJSP) with transportation is presented, aiming to minimize the total service duration time for flights, the total waiting time of logistics vehicles, and the imbalance of logistics vehicles usage. To solve AGCTS, an algorithm based on the elitist nondominated sorting genetic algorithm version II (NSGAII), named NSGAII-GRSr is developed. With a problem-based triple-layer encoding method and a state-based decoding operator, a bridge is built between the chromosome representation and the solution. To improve the algorithm performance, a Greedy-Random Selection (GRS) strategy is integrated in population initialization and a crossover operator rPOS is developed. Besides, a benchmark with 225 instances for AGCTS is designed, on which experimental study is conducted. The experimental results confirm the validity of the proposed multi-objective model and the superior performance of NSGAII-GRSr over the original NSGAII as well as five other well-known algorithms in solving AGCTS.