基于鲸鱼优化算法的PVR条件下共享停车供需匹配研究
Research on Shared Parking Supply-Demand Matching under PVR Conditions Based on Whale Optimization Algorithm
摘要: 为了使有人驾驶车辆充分利用无人车自主移位的特征，本文引入泊位机器人(PVR)辅助车辆变换泊位以提高泊位利用率，降低系统成本以及减少事故发生的概率。针对减少PVR辅助车辆变换泊位而产生的移位次数和距离，建立了单停车场或特定停车区域下的满足可接受停车需求的预约式共享停车供需匹配模型，该问题可视为特殊的二次分配模型。考虑到本模型具有NP-hard特征，本文设计了对应的鲸鱼优化算法，数值分析不仅验证了该模型的合理性和算法的高效，而且与随机的匹配的初始结果相比，该算法优化后的成本结果减少了20%。从解决城市“停车难”的立场看，本研究为以有人驾驶车辆为对象的共享停车研究实践提供了强有力的思路和方法。
Abstract: To fully leverage the self-shifting capability of autonomous vehicles when operated by human driv-ers, this study introduces Parking Vehicle Robots (PVRs) to assist in optimizing parking space allo-cation. The aim is to enhance parking space utilization, reduce overall system costs, and mitigate the occurrence of accidents. To address the challenge of minimizing the frequency and distance of PVR-assisted vehicle relocations, a reservation-based shared parking supply-demand matching model is developed. This model is applicable to single parking lots or specific parking zones, cater-ing to the acceptable parking demands. The problem at hand is akin to a specialized quadratic as-signment problem. Recognizing the inherent complexity of this model (NP-hard), an innovative Whale Optimization Algorithm (WOA) is crafted. Through comprehensive numerical analysis, not only is the model's rationality established and the algorithm's efficiency demonstrated, but the re-sults also indicate a remarkable 20% reduction in optimized cost when compared to initial out-comes from random matching. From the standpoint of addressing the prevalent urban parking challenge, this research contributes a robust framework and approach for practical studies in shared parking, particularly focused on human-driven vehicles.
文章引用：贾田峰, 何胜学, 章炎, 李瑞康. 基于鲸鱼优化算法的PVR条件下共享停车供需匹配研究[J]. 建模与仿真, 2023, 12(5): 4696-4706. https://doi.org/10.12677/MOS.2023.125428

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