This study proposes a multiobjective mixed integer linear programming (MOMILP) model for a demand-responsive airport shuttle service. The approach aims to assign a set of alternative fuel vehicles (AFVs) located at different depots to visit each demand point within the specified time and transport all of them to the airport. The proposed model effectively captures the interactions between path selection and environmental protection. Moreover, users with flexible pick-up time windows, the time-varying speed of vehicles on the road network, and the limited fuel for the route duration are also fully considered in this model. The work aims at simultaneously minimizing the operating cost, vehicle fuel consumption, and CO₂ emissions. Since this task is an NP-hard problem, a heuristic-based nondominated sorting genetic algorithm (NSGA-II) is also presented to find Pareto optimal solutions in a reasonable amount of time. Finally, a real-world example is provided to illustrate the proposed methodology. The results demonstrate that the model not only selects an optimal depot for each AFV but also determines its route and timetable plan. A sensitivity analysis is also given to assess the effect of early/late arrival penalty weights and the number of AFVs on the model performance, and the difference in quality between the proposed and traditional models is compared.

中文翻译：

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时变速度的绿色响应型机场班车服务问题

这项研究为需求响应机场班车服务提出了一个多目标混合整数线性规划（MOMILP）模型。该方法旨在分配一组位于不同仓库的代用燃料汽车（AFV），以在指定时间内访问每个需求点并将其全部运输到机场。所提出的模型有效地捕捉了路径选择与环境保护之间的相互作用。此外，在该模型中还充分考虑了具有灵活的上车时间窗口，道路网络上车辆的时变速度以及路线持续时间有限的燃料的用户。该工作旨在同时最大程度地降低运营成本，车辆燃油消耗和CO ₂排放。由于此任务是NP难题，因此还提出了一种基于启发式的非支配排序遗传算法（NSGA-II），以在合理的时间内找到Pareto最优解。最后，提供了一个实际示例来说明所提出的方法。结果表明，该模型不仅为每个AFV选择了一个最佳仓库，而且还确定了其路线和时间表。还进行了敏感性分析，以评估早/晚到达罚款权重和AFV数量对模型性能的影响，并比较了建议模型和传统模型之间的质量差异。