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Research on airport reservation bus scheduling
Investigación sobre la programación de autobuses de reserva en aeropuertos
DOI:
https://doi.org/10.15446/dyna.v92n238.118471Palabras clave:
airport reservation bus, differential pricing, vehicle scheduling, improved adaptive genetic algorithm (en)autobús con reserva de aeropuerto, tarificación diferencial, programación de vehículos, algoritmo genético adaptativo mejorado (es)
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Passengers face high costs or multiple transfers when they arrive or depart from the airport. In addition, most transportation modes in a city typically stop operating from midnight to early morning, making it impossible for passengers to enjoy quality and inexpensive services. Considering the passenger detour and muti-types, this paper sets up the passenger detour rebate mechanism and constructs the Airport Reservation Bus (ARB) scheduling model to maximize the profit of ARB enterprise. Meanwhile, an Improved Adaptive Genetic Algorithm (IAGA) is designed to solve the model, where the crossover and mutation operations are optimized to prevent it from falling into local optimum. Finally, a case study shows that ARB costs at least 39% less than taxis, with slightly longer travel time. Compared to traditional GA, IAGA reduced running time by more than 12%, showing faster convergence.
Los pasajeros afrontan altos costos o múltiples trasbordos al viajar al aeropuerto. Además, la mayoría de los medios de transporte urbano cesan su operación de medianoche a primera hora, privando a los pasajeros de servicios económicos y de calidad. Considerando desvíos y tipos mixtos de vehículos, se propone un mecanismo de reembolso por desvío y un modelo de programación de autobuses de reserva aeroportuaria (ARB) para maximizar la rentabilidad. Se diseña un Algoritmo Genético Adaptativo Mejorado (IAGA) optimizando cruce y mutación para evitar el óptimo local. Los casos muestran que el ARB cuesta 39% menos que los taxis con un ligero aumento de tiempo. En comparación con el AG tradicional, el IAGA redujo el tiempo de ejecución en más del 12%, lo que demuestra una convergencia más rápida.
Referencias
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