<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; mso-layout-grid-align: none;" align="left"><span style="mso-bidi-font-weight: bold;"><span style="font-size: x-small;"><span style="font-family: Times New Roman;">Pool segmentation is an essential step in the logistics management process for large-scale car rental business. Its main function involves dynamic decisions about pools clustering and regional logistics management centers selecting, whose goal is to optimize fleet utilization and improve the logistics management efficiency. According to developing mode of car rental enterprises and layout of leasing sites, a three-tier structure is presented to describe the logistics management relations among each leasing sites. Based on the logistics operation characteristics and practical administration demand, a dynamic model and its algorithm are proposed for pool segmentation in the car rental industry. A case study shows that the proposed methodology is feasible and effective. <em></em></span></span></span></p>

The unconstrained demand forecast for car rentals has become a difficult problem for revenue management due to the need to cope with a variety of rental vehicles, the strong subjective desires and requests of customers, and the high probability of upgrading and downgrading circumstances. The unconstrained demand forecast mainly includes repairing of constrained historical demand and forecasting of future demand. In this work, a new methodology is developed based on multiple discrete choice models to obtain customer choice preference probabilities and improve a known spill model, including a repair process of the unconstrained demand. In addition, the linear Holt–Winters model and the nonlinear backpropagation neural network are combined to predict future demand and avoid excessive errors caused by a single method. In a case study, we take advantage of a stated preference and a revealed preference survey and use the variable precision rough set to obtain factors and weights that affect customer choices. In this case study and based on a numerical example, three forecasting methods are compared to determine the car rental demand of the next time cycle. The comparison with real demand verifies the feasibility and effectiveness of the hybrid forecasting model with a resulting average error of only 3.06%.

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