考虑共同配送和能耗的车辆路径问题优化研究

发布时间：2018-08-25 09:01

【摘要】：共同配送是配送发展的重要趋势之一,其价值在于通过合作、资源共享降低物流成本、提高物流效率,同时削减在途运行车辆,缓解城市交通压力,节约社会资源、减轻环境污染。由于其存在的重要价值,共同配送在发达国家(如德国、日本)得到了广泛的实践应用,在我国,共同配送尚处于起步阶段,随着市场经济的发展,物流环境的不断成熟以及物流成本、交通拥挤带来的持续压力,将促使共同配送在我国迅速发展。然而,共同配送企业在实际运作中,存在着一些问题亟待解决,例如,如何整合现有配送资源,如何降低配送车辆能耗,发挥更大的效益?本文首先分析了共同配送的运作模式,通过对现有模式的分析得出共同配送的资源整合主要体现为三个方面：客户资源、配送车辆资源和配送点资源,并在此基础上建立了几类共同配送策略。其次,根据客户资源共享的情况,考虑区域经济一体化过程中,供应链的供应企业或零售连锁企业等,当其公司发展到一定的规模后,存在有多个配送中心实行共同配送的情况,以满足客户差异性的商品需求,和日渐小批量、多批次的配送需求,需要考虑基于客户差异性的情况来整合客户资源,提高配送效率和服务水平。文中构建了客户分组条件下的多配送点车辆路径模型,以配送距离最短为优化目标,在遗传算法的基础上进行算法设计,通过设计插入变异改变配送中心客户群数量,提高路径搜索的广度,并将改进算法与传统算法在运行结果上进行了对比分析,并进一步分析了不同种群下的寻优过程。再次,根据配送车辆共享的情况,考虑实际配送运作中,客户需求的变化性,配送车辆在完成每阶段最后一个客户点的配送服务后,无需回到出发车场,可选择就近车场停靠或依据资源共享降低成本的原则,将车辆停靠在开放的协同企业车场。每次配送阶段均为独立的,随着客户需求的变化,配送车场的配送车辆随着不断变化的配送需求而调整车场车辆数。文中构建了车场开放条件下的多配送点车辆路径模型,以配送距离最短为优化目标,在粒子群算法的基础上进行了算法设计,通过对粒子更新进行设计,提高算法的搜算广度,避免陷入局部最优,并将改进算法与传统粒子群算法进行对比分析。最后,讨论配送车辆在实际的配送行驶中,由于配送距离的变化,配送时间的约束,以及实际配送速度与城市车辆限速等不同情况下的车辆行驶能耗分析,并进行了仿真测试,为城市配送车辆在距离、时间、速度等不同条件下,选择节能低碳的配送车辆行驶模式提供参考依据。
[Abstract]:Joint distribution is one of the important trends in the development of distribution. Its value lies in reducing logistics cost and improving logistics efficiency through cooperation, resource sharing, and at the same time reducing the running vehicles on the way, relieving the pressure of urban traffic and saving social resources. Reduce environmental pollution. Because of its important value, joint distribution has been widely used in developed countries (such as Germany, Japan). In China, joint distribution is still in its infancy, with the development of market economy. The maturation of logistics environment and the continuous pressure of logistics cost and traffic congestion will promote the rapid development of joint distribution in China. However, in the actual operation of joint distribution enterprises, there are some problems to be solved. For example, how to integrate the existing distribution resources, how to reduce the energy consumption of distribution vehicles, and how to play a greater benefit? This paper first analyzes the operation mode of joint distribution, and through the analysis of the existing model, it is concluded that the integration of common distribution resources is mainly reflected in three aspects: customer resources, distribution vehicle resources and distribution point resources. On this basis, several common distribution strategies are established. Secondly, according to the situation of customer resource sharing, considering the process of regional economic integration, supply enterprises or retail chain enterprises in the supply chain, when their companies develop to a certain scale, There are many distribution centers to implement joint distribution to meet the needs of different customers, and smaller and more batches of distribution needs, need to consider the situation based on customer differences to integrate customer resources, Improve delivery efficiency and service level. In this paper, a multi-point vehicle routing model under the condition of customer grouping is constructed. With the shortest distribution distance as the optimization goal, the algorithm is designed on the basis of genetic algorithm, and the number of customers in distribution center is changed by inserting variation. The improved algorithm is compared with the traditional algorithm in the running results, and the optimization process under different populations is further analyzed. Thirdly, according to the situation of distribution vehicle sharing, considering the variability of customer demand in actual distribution operation, the distribution vehicle does not need to return to the departure yard after completing the distribution service of the last customer point in each stage. According to the principle of reducing the cost of resource sharing, the vehicle can be parked in the open cooperative enterprise yard. Each distribution stage is independent, with the change of customer demand, the distribution vehicle of the distribution yard adjusts the number of vehicles with the changing distribution demand. In this paper, the vehicle routing model of multi-distribution points under the condition of open vehicle yard is constructed. With the shortest distribution distance as the optimization goal, the algorithm is designed on the basis of particle swarm optimization algorithm, and the search breadth of the algorithm is improved by designing the particle update. To avoid falling into local optimum, the improved algorithm is compared with the traditional particle swarm optimization algorithm. Finally, the paper discusses the energy consumption analysis of distribution vehicles under different conditions, such as the variation of distribution distance, the constraint of distribution time, and the actual distribution speed and the speed limit of urban vehicles, and carries out simulation tests. This paper provides a reference for the choice of energy saving and low carbon distribution vehicle driving mode under different conditions such as distance, time, speed and so on.
【学位授予单位】：西南交通大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：F252

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