To address challenges inherent in the scheduling of public bus transportation, such as disparities in peak and off-peak operational demands, amalgamated single and double shift operations, this study endeavors to mode...
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ISBN:
(纸本)9798350389913;9798350389906
To address challenges inherent in the scheduling of public bus transportation, such as disparities in peak and off-peak operational demands, amalgamated single and double shift operations, this study endeavors to model and optimize the daily operational schedules for bus routes, grounded in empirical data reflecting actual operational requirements. The formulation of the bus scheduling problem entails the integration of various parameters including planned trip quantities, peak-hour road conditions, station dwell durations, inter-departure intervals, and driver shift change dynamics. In the optimization process, a hybrid algorithm, combining geneticalgorithm (GA) and tabusearch (TS), is proposed. GA serves as a heuristic for a global exploration of the solution space, while TS is for a detailed exploration of local regions. Leveraging the operational parameters of actual bus schedules in Nanjing, the proposed hybrid algorithm is applied to refine the scheduling plan for a specific bus route. The actual scheduling results demonstrate that, in comparison to stand-alone implementations of GA, greedy algorithms, and manually crafted schedules, the hybrid GA-tabualgorithm yields a noteworthy 7.88 percent improvement in the utilization rate of working hours. Furthermore, the departure frequency seamlessly adapts to peak periods, aligning with passenger demand patterns and augmenting the overall system efficiency. The proposed hybrid GA-tabualgorithm proves efficacious in enhancing system efficiency, catering to passenger demands, and ensuring compliance with driver work hour regulations. Besides, its applicability showcases a degree of generality within the realm of public bus transportation scheduling.
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