考虑多方利益的大规模共享停车匹配优化策略

A Massive Shared Parking Matching Optimization Strategy Considering the Interests of Multiple Parties

为有效缓解大规模停车时的停车乱、停车难问题,围绕停车费用、排队时间以及均衡多个停车场之间的停车需求,构建兼顾多方利益凸优化模型。首先,利用匹配博弈推导出最小化停车费用的稳定双边匹配;然后,同时考虑多方利益来扩展研究,构建的数学模型为凸优化问题,使用交替方向乘子法(ADMM)分布式求解;最后,仿真结果表明基于ADMM分布式优化模型与匹配博弈算法和贪心算法相比,可以满足多方利益,从隐私保护、计算时间与数据传递量等方面分析验证了ADMM分布式优化模型比集中式优化模型更适用于大规模共享停车匹配,并分析验证了考虑多方利益的必要性与权重系数对计算结果的影响。

In order to effectively alleviate the problem of parking chaos and difficult parking during massive parking,this paper builds a convex optimization model that takes into account the interests of multiple parties,focusing on parking costs,queuing time and balancing the parking demand among multiple parking lots.First,a stable bilateral matching that minimizes parking fees is derived by using a matching game;then,the research is extended considering the interests of multiple parties at the same time,and the mathematical model constructed is a convex optimization problem,which is solved by the alternating direction multiplier method(ADMM)distributed solution.Finally,the simulation results show that the distributed optimization model based on ADMM can meet the interests of multiple parties compared with the matching game algorithm and the greedy algorithm.It is verified that the ADMM distributed optimization model is more efficient than the centralized optimization model from the aspects of privacy protection,computing time and data transfer volume.It is suitable for massive shared parking matching,and the necessity of considering the interests of multiple parties and the influence of weight coefficients on the calculation results are analyzed and verified.