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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Veröffentlicht in:Journal of advanced transportation Jg. 2020; H. 2020; S. 1 - 13
Hauptverfasser: Zang, Yang, Yin, Jian, Jing, Binbin, Wei, Ming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Wiley
Schlagworte:
Airport planning
Airports
Alternative fuels
Carbon
Carbon dioxide
Carbon dioxide emissions
Customer services
Demand
Energy consumption
Environmental impact
Environmental protection
Genetic algorithms
Heuristic methods
Integer programming
Mixed integer
Operating costs
Pareto optimum
Random variables
Ridesharing
Roads & highways
Route selection
Sensitivity analysis
Sorting algorithms
Timetables
Traffic
Traffic congestion
Traffic speed
Transportation
Transportation networks
Vehicle emissions
Vehicles
Windows (intervals)
ISSN:0197-6729, 2042-3195
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0197-6729
2042-3195
DOI:10.1155/2020/9853164