针对交通限行条件下城市配送的现实问题，提出基于车辆协作的"多对多"网络化换乘联运策略。设计协作点的选择准则与序贯式联运规则，以协作点衔接城市通行区域与限行区域，建立基于车辆协作的城市配送换乘联运模型。同时，考虑到模型的复杂性，利用云模型云滴的随机性与倾向性，改进遗传算法中变异与交叉概率的设置方式，设计云遗传算法优先求解第二级配送问题，再利用C-W算法求解第一级配送问题，为了增强算法的求解质量与效率，设计了扰动算子与种群扩张算子。最后，结合不同算例验证了模型与算法的有效性。
Urban distribution is a complex ecosystem, which bears the interactive features of the city and the outside world, it guaranteed of the development of city economic and the better of Urban residents. In recent years, however, with the sharp increase of cars in city and road resources nervous, which caused serious traffic congestion in large cities. In order to alleviate traffic pressure, especially peak period congestion in road, domestic and overseas cities have adopted some methods to solve this problem, for example, Night deliveries, Restricting truck. However it is difficult for urban distribution and bring new challenges to the city logistics distribution. In order to deal with traffic restrictions policies for different vehicle types in different route section and different time section, characteristics of route, vehicles, demand are analyzed in depth and then urban distribution under such traffic restrictions is studied from the perspective of optimal Route-Time-Vehicle-Demand matching. In the first place, the incoherence in distribution caused by traffic restriction is about to be solved by studying partitioning distribution, matching method between vehicle types and traffic routes, interchange rules in multi-modal transportation. Corresponding collaborative strategies for vehicles and sequential interchanges strategies will be proposed. Secondly, the economic and flexibility of different vehicle types can be fully made use via introducing the freight route concept, also, the restricted traffic area and artery traffic network are connected by stripping and combining freight route, setting interchange point and coordinating the vehicles. The two-stage optimization model is built and according cloud quantum genetic algorithm is designed to provide quantitative studies for logistics distribution in this vehicle collaboration and integrated transportation problem. Meanwhile, using the randomness and bias stability of the cloud droplet cloud model, the Cloud Genetic algorithm is designed for the secondary distribution problem and the C-W algorithm is designed for the first level distribution problem, in order to enhance the solving quality and efficiency of algorithm, the disturbance operator and population expansion operator are designed. For the comparison, several different cases are conducted to illustrate the established model and solving algorithm.
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