【摘要】 针对平面移动类立体车库在车辆存取效率方面的瓶颈问题,提出了一种基于PSO-OBL算法的存取车辆调度优化模型。该模型旨在通过精确调控车辆存取策略和时间管理,缩短车辆存取运行时间及用户平均等待时间。为提升传统粒子群算法的寻优效能和收敛速率,将粒子间相互协作与信息交流机制融入算法框架,并结合反向学习机制以实现问题的高效求解。实验数据表明,与传统粒子群算法相比,PSO-OBL算法在顾客平均等待时间、平均服务时间、平均等待队长以及平均运行能耗等方面均实现了显著提升,研究结果将为平面移动类立体车库的存取效率提供优化理论支持和实践参考。更多还原

【Abstract】 To address the bottleneck issues in vehicle access efficiency for horizontal shifting mechanical parking garages, an access vehicle scheduling optimization model based on the PSO-OBL algorithm was proposed. The model aims to shorten vehicle access operation time and reduce user average waiting time by precisely controlling vehicle access strategies and time management. To enhance the optimization performance and convergence rate of the traditional particle swarm optimization algorithm, an innovative approach incorporating inter-particle collaboration and information exchange mechanisms was embedded into the algorithm framework, along with the integration of an opposition-based learning mechanism for efficient problem-solving. Experimental data indicates that, compared to the traditional particle swarm optimization algorithm, the PSO-OBL algorithm achieves significant improvements in customer average waiting time, average service time, average queue length, and average energy consumption. The findings of this study are expected to provide theoretical support and practical reference for optimizing access efficiency in horizontal shifting mechanical parking garages.更多还原