部分需求信息下分布式鲁棒车辆配置优化

Distributionally Robust Optimization for Shared Vehicles Allocation under Partial Demand Information

近年来,在共享经济及供给侧改革的时代背景下,车辆共享服务成为解决城市交通拥堵问题的创新服务模式,然而在实际运营过程中诸多不确定性因素降低其运营效率。本文以电动车为主要构成的车辆共享服务为研究对象,在初始车辆配置阶段,考虑不确定需求的部分矩信息,结合最小化最坏情形的思想,建立分布式鲁棒车辆配置模型。同时分别考虑"确定租赁需求"、"随机租赁需求"、"部分不确定租赁需求矩信息已知"三类情况,建立相应的车辆共享服务车辆配置模型,并结合北京市15个租赁点信息,对三类模型算例决策结果进行对比,通过蒙特卡洛模拟比较相应的配置量及车辆配置成本。结果表明本文提出的方法相对于另外两类模型具备更好鲁棒性,同时可较好地在不确定环境下节约运营成本,具备较强实际应用价值,运营商可通过部分需求信息对初期车辆配置进行决策。

In recent years, under the background of sharing economy and supply-side reform, vehicle sharing sex-vice becomes an innova- tive service model to mitigate the urban traffic congestion. However, in the process of vehicle sharing services operation, there are many uncertain factors which make the normal operation less efficient. To explore the approaches to solve this problem, this paper takes the e- lectric vehicle as the main component of vehicle sharing service. In the initial stage of vehicle configuration, considering the partial mo- ment information under the uncertain requirement, and combining the minimizing the worst-case theory, we propose distributionally ro- bust vehicle allocation model. Further, partly considering three categories of situations namely ＂determined rental requirement＂, ＂sto- chastic rental requirement＂ and ＂partial uncertain rental requirement with known moment information＂, we also put forward correspond- ing vehicle sharing service vehicle configuration model, compare the results of the three models combining information of the 15 rental point in Beijing, and compare the corresponding allocation quantities and vehicle allocation cost through Monte Carlo simulation. The re- suits show that the proposed method has better robustness relative to the other two models. At the same time, it can better reduce operat- ing costs in an uncertain environment. It has strong practical application value for operators to make decisions on initial vehicle allocation through partial demand information.