A Green Demand-Responsive Airport Shuttle Service Problem with Time-Varying Speeds

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 a...

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Bibliographic Details
Published in:	Journal of advanced transportation 2020, Vol.2020, p.1-13
Main Authors:	Wei, Ming, Jing, Binbin, Yin, Jian, Zang, Yang
Format:	Article
Language:	eng
Subjects:	Airport planning Airports Alternative fuels Carbon Carbon dioxide Customer services Demand Energy consumption Environmental impact Environmental protection Genetic algorithms Heuristic methods Integer programming Mixed integer Operating costs Random variables Roads Roads & highways Route selection Sensitivity analysis Sorting algorithms Timetables Traffic congestion Traffic speed Transportation networks Vehicle emissions Vehicles Windows (intervals)
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Summary:	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.
ISSN:	0197-6729 2042-3195