Abstract
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.
Highlights
Airports are generally located in the suburbs of a city; passengers living in urban areas need to be transported to the airport by shuttle buses
E main contribution of this paper is to investigate a multiobjective green demand-responsive airport shuttle services (DRASS) with a time-varying network in order to minimize operating cost, vehicle fuel consumption, and CO2 emissions. e main research tasks are summarized as follows: (1) coordination of a DRASS transit routing and departure time guidance process based on a time-varying network to balance path selection and environmental protection; (2) development of a heuristic-based NSGA-II algorithm to efficiently obtain a set of Pareto optimal solutions
Both demand-responsive transit system (DRT) and DRASS are extensions of the vehicle routing problem (VRP) and the pick-up and delivery problem (PDP), which aim at assigning all customers in demand points to vehicles located at different bus depots and designing routes to transport them from their home or workplace to destinations [1, 2, 5, 8]. e only difference between them is that DRT strengthens the connectivity between residential areas and rail stations, while DRASS transports air passengers to airports [1, 7]. ey have similar objective functions and constraints
Summary
Airports are generally located in the suburbs of a city; passengers living in urban areas need to be transported to the airport by shuttle buses. As with DRT, the increase in consciousness about environmental impact has made green DRASS a critical issue In this case, DRASS route decisions determine operating cost, vehicle fuel consumption, and carbon emissions [4, 5]. Remote vehicle tracking techniques have been used to collect detailed traffic data on transit times for different roads by time of day and day of the week, which provides the possibility for the second models to accurately estimate carbon emissions. E main contribution of this paper is to investigate a multiobjective green DRASS with a time-varying network in order to minimize operating cost, vehicle fuel consumption, and CO2 emissions.
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