Abstract This study introduces an innovative two-stage optimization model for Electric Towing Vehicle (ETV) scheduling at airports, aiming to resolve the conflict between resource investment and operational efficiency. Given that aircraft ground taxiing contributes significantly to aviation carbon emissions, with about 7% of total flight fuel consumed during this phase, ETVs emerge as a key solution for the industry’s low-carbon transition.The first stage of the model minimizes the number of ETVs, setting a baseline for resource input. The second stage focuses on optimizing task allocation, with two schemes: one prioritizing minimum scheduling time and the other aiming for optimal task allocation equilibrium. The model addresses gaps in existing studies by integrating flight imbalance simulation (using the Cellular Automaton-CA model) and dynamic hub allocation, which were previously overlooked. The Genetic Greedy Hybrid Algorithm (GGHA) is applied to ensure global optimality and efficient task allocation , with CA simulating real-world airport operations to determine flight service windows for model input.A case study at Tianjin Binhai International Airport validates the model’s effectiveness. The proposed method reduces ETV requirements by approximately 19.4% compared to the current scheme. It offers two distinct schemes for comparison within the second stage: the minimum scheduling time scheme shortens total scheduling time by about 1.1%, while the optimal task allocation equilibrium scheme reduces task load variance by 82%, enhancing resource utilization and long-term system stability. This research provides a practical framework for airports to enhance ETV scheduling efficiency and achieve sustainable development.
Liu et al. (Wed,) studied this question.