物流车辆节能配送路径优化算法研究

发布时间：2018-06-05 21:32

本文选题：车辆路径问题 + 时间窗　；参考：《北京交通大学》2015年硕士论文

【摘要】：近年来,物流业在社会经济发展中发挥着越来越重要的作用,同时也带来了严重的能源和环境问题。合理规划物流车辆的配送路径被视为降低物流企业运营成本、缓解能源短缺和环境污染的重要途径之一。本文以物流车辆节能配送路径优化问题为研究对象,综合考虑配送时间窗要求、车辆配载约束、载重和速度对车辆能耗因子的影响,构建整数规划模型,并利用改进的蚁群算法进行求解,以最少的能源消耗实现节能配送。本文的主要研究内容如下：首先,按照不同分类标准将车辆路径优化问题进行分类,并对常用算法的特点进行分析。其次,考虑货车受车厢尺寸和最大载重量的限制、车辆能耗因子受车速和货车载重的影响,分别构建三维装箱数学模型和车辆综合油耗计算模型。进而,以三维装箱模型为约束条件,以配送方案油耗最低为优化目标,构建考虑三维装箱和时间窗约束的时间依赖型节能配送路径优化问题数学模型。再次,针对以上模型,对蚁群算法进行了如下改进：第一,在考虑客户点周边路网条件、货物需求特征的基础上,提出可将车辆临时停靠在道路另一侧的备选停靠点处,以减少因配送车辆只能停靠在客户所在道路一侧可能造成的迂回运输,进而减少能耗；第二,利用模拟退火算法求解三维装箱约束模型,在满足车辆载重约束的同时,也满足货物不相互重叠、先卸后装等约束；第三,以油耗为标准更新蚁群信息素浓度,达到逐步优化配送方案、降低油耗的目的；第四,基于初步优化结果,考虑车速对车辆能耗因子的影响,结合路网动态交通信息,通过调整配送车辆从配送中心及各客户点的出发时刻,实现对配送方案总油耗的进一步优化。最后,利用北京市实际路网和动态交通数据,构造6个不同客户点数量规模的配送案例,并采用以上所提出的数学模型和蚁群算法进行求解。计算结果表明,相对于传统方法,本文所提出的优化方法可使物流配送油耗量最多可降低25.52%。
[Abstract]:In recent years, the logistics industry plays a more and more important role in the social and economic development, but also brings serious energy and environmental problems. The rational planning of the distribution path of logistics vehicles is regarded as one of the important ways to reduce the operating cost of logistics enterprises and alleviate the energy shortage and environmental pollution. In this paper, we take the optimization of energy saving distribution path of logistics vehicles as the research object, consider the requirements of distribution time window, the influence of vehicle loading constraints, load and speed on vehicle energy consumption factors, and construct an integer programming model. The improved ant colony algorithm is used to solve the problem and energy saving distribution is realized with the least energy consumption. The main contents of this paper are as follows: Firstly, the vehicle routing optimization problem is classified according to different classification criteria, and the characteristics of common algorithms are analyzed. Secondly, considering the limitation of truck size and maximum load, and the influence of vehicle energy consumption factor on vehicle speed and truck load, a three-dimensional packing mathematical model and a vehicle comprehensive fuel consumption calculation model are constructed respectively. Furthermore, taking the three-dimensional packing model as the constraint condition and the lowest fuel consumption of the distribution scheme as the optimization objective, the mathematical model of time-dependent energy saving distribution path optimization problem considering three-dimensional packing and time window constraints is constructed. Thirdly, according to the above model, the ant colony algorithm is improved as follows: first, considering the network conditions around the customer points and the characteristics of the cargo demand, the paper proposes that the vehicles can be temporarily parked at alternative stops on the other side of the road. In order to reduce the circuitous transportation caused by the distribution vehicles only parked on one side of the road where the customer is, and then reduce the energy consumption. Secondly, the simulated annealing algorithm is used to solve the three-dimensional packing constraint model, which satisfies the vehicle load constraints at the same time. It also meets the constraints of goods not overlapping, first unloaded and then loaded; third, the ant colony pheromone concentration is updated according to fuel consumption to achieve the purpose of gradually optimizing the distribution scheme and reducing fuel consumption; fourth, based on the preliminary optimization results, Considering the influence of vehicle speed on vehicle energy consumption factor, combined with the dynamic traffic information of road network, the total fuel consumption of distribution scheme can be further optimized by adjusting the departure time of distribution vehicle from distribution center and each customer point. Finally, based on the actual road network and dynamic traffic data in Beijing, six distribution cases with different number of customer points are constructed, and the above mathematical model and ant colony algorithm are used to solve the problem. The results show that compared with the traditional method, the optimization method proposed in this paper can reduce the fuel consumption of logistics distribution by 25.52%.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U492.22

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