Dynamic Scheduling Based on Predicted Inventory Variation Rate for Public Bicycle System

Abstract

To improve efficiency and reduce the total scheduling cost of the public bicycle system (PBS), dynamic scheduling based on the predicted inventory variation rate (DS-PIVR) is proposed. Regarding a station in the PBS as an inventory system, its inventory variation rate during the scheduling period and its inventory rate at the end of the scheduling period were predicted based on the stationary Markov process condition. A mixed integer programming (MIP) model, whose objective is to minimize the total scheduling distance, was established to describe the dynamic scheduling problem (DSP). Results from Boston and Washington D.C. PBSs show that, when compared to the dynamic scheduling based on the rolling horizon (DS-RH), the DS-PIVR method could at most shorten the routing distance by 62.25% (for Boston) and 74.7% (for Washington D.C.) among all scheduling areas, and could at most shorten the total routing distance for the whole PBS by 21.06% (for Boston) and 17.26% (for Washington D.C.). Moreover, the DS-PIVR method makes the repositioning vehicle journey only once and keeps the inventory rate of each station in balance during the scheduling period. Furthermore, the DS-PIVR method provides a promising reference to improve the operation efficiency by reducing the scheduling cost and the quality of service by satisfying the users’ demand in time during the rush hours for the PBS operators.

Hierarchical Vehicle Scheduling Research on Tide Bicycle-Sharing Traffic of Autonomous Transportation Systems

“…To further verify the reliability of this model, Figure 4 shows the comparison for iteration between the HATB and other models proposed in the literature with similar objectives. Specifcally, based on the GA, Gao et al [35] provided a promising perspective on improving operation efciency by reducing operating costs and service quality during peak times to minimize the total operating a0-a11-a4-a18-a17-a9-a1-a12-a15-a19-a5-a0 170 (a0) Te proposed HATB converged at the 64 th generation, and the total time cost is 148.9 s, with an average running time of 15 s per generation. Due to the diferent objectives, only the convergence speed of the above models is compared.…”