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

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.

Shuttle bus service routing: A systematic literature review

“…# of travelling time [18], [19], [23], [24], [30], [32], [33], [35], [36], [39], [42], [46], [47], [58], [61], [66], [73], [76] min. the total travelling cost [8], [11], [12], [20]- [22], [24], [27], [28], [30], [39], [40], [42], [44], [45], [48], [51], [54], [55], [57], [59], [68], [72], [81] min. the rental cost [28], [59] min.…”

Optimization of Vehicle Paths considering Carbon Emissions in a Time-Varying Road Network

“…Similarly, Wei et al fully considered the time-varying speed and time window of the vehicle and proposed a heuristic-based nondominated ranking genetic algorithm to find the Pareto optimal solution in a reasonable time [18]. Liu Zhang constructed an urban cold chain transportation model, with the objective function of minimizing the total cost in view of the time-dependent nature of urban cold chain transportation and the time-varying nature of urban road speed, and an artificial immune particle swarm optimization algorithm to solve the model [19].…”

Optimizing Centralized Dispatching of Flexible Feeder Transit considering Transfer Coordination with Regular Public Transit

“…Constraint (11) defines the mathematical relationship between the departure and arrival times of each bus run serial number of the feeder transit. Constraint (12) defines the mathematical relationship between the arrival time of a bus run serial number of the feeder transit and the express line, which guarantees that the passenger waiting time is zero.…”

“…Some studies have proposed machine learning [27], hybrid evolutionary [28], and Tabu search [29,30] methods for the dial-a-ride problem; GA [12,31] and heuristic algorithm [11,24] are among the most successful approximate approaches for solving the dial-a-ride problem. GA was tested on a set of medium-scale problem scenarios, and it was found to be accessible and efficient.…”

“…Moreover, Molenbruch et al [9] and Oscar et al [10] studied DART services for special travel demands, e.g., medical users with wheelchairs. Problems related to actual dispatching and new technology have been analyzed and considered in many studies, e.g., the time-varying speed of vehicles on the road network (Schilde et al [11], Wei et al [12]), flexing service schedules for demand-adaptive hybrid transit (Frei et al [13]), autonomous vehicles (Jager et al [14]), and roundtrip car-sharing systems (Jorge et al [15]).…”