基于改进粒子群算法的配送中心车辆优化调度问题研究

发布时间：2018-11-02 12:21

【摘要】：近年来,随着电子商务的发展,并且随着货运改革的不断深入,加快铁路向现代物流企业的建设,物流行业再次迎来了大发展,物流配送在经济活动中的作用也愈发突显。而发展往往伴随着问题,因此,作为物流配送中的重要问题——车辆调度问题重新受到了学者们的关注。车辆调度问题已经发展了几十年,但是随着社会的不断发展,仍然有一些具有鲜明时代特征的新问题不断涌现,尤其是电商物流的大力发展,客户满意度在物流配送中愈发显得重要,对企业的影响越来越大。配送作为一种具有服务性质的行业,对车辆调度问题进行优化,不单是对配送企业的成本优化,更是为了保障服务的高效性,提升客户的满意指数,进而提高配送企业的竞争力,使其能够激烈的竞争中获得长远的发展。本文在参阅了已有的相关文献及研究成果的基础上,建立了针对客户满意度评价的车辆优化调度问题模型,并运用粒子群算法和改进粒子群算法分别对模型的实例进行了求解,以验证算法的有效性。具体如下:(1)模型构建方面。为了更好的对客户的满意度进行评估,使得模型更加贴近现实,本文引入梯形模糊时间函数,利用客户所期望的服务时间以及允许的服务时间这两个时间段对客户的满意度进行评价,并且考虑成本和时间因素,最终建立了在满足客户满意度最大的情况下,使得时间和成本最小的多目标模型。(2)求解算法方面。首先,对求解车辆调度问题的算法进行了详细的研究,通过求解算例对算法进行对比分析,说明不同算法的优缺点。然后,针对标准粒子群算法的缺陷,引进菌群算法中的复制和迁移算子,对其进行改进,使用标准测试函数对算法进行了验证,通过测试可以得出,改进粒子群算法较标准粒子群算法而言,具有更强的搜索能力,并且有一定的能力跳出局部最优。最后,为了更好的使用改进粒子群算法求解模型,对粒子的编码方式,进化方式进行了改进,对权重方案的选择进行了讨论,最终通过对实例求解效果的分析对比,证明了改进粒子群算法在求解车辆调度问题中的有效性。
[Abstract]:In recent years, with the development of electronic commerce and the deepening of freight transport reform, the construction of railway to modern logistics enterprises has been accelerated, the logistics industry has again ushered in a great development, and the role of logistics distribution in economic activities has become increasingly prominent. But the development often accompanies the question, therefore, as the important problem in the logistics distribution, the vehicle scheduling problem has been paid more attention by the scholars. Vehicle scheduling problem has been developed for several decades, but with the continuous development of society, there are still some new problems with distinct characteristics of the times, especially the development of e-commerce logistics. Customer satisfaction is becoming more and more important in logistics distribution and has more and more influence on enterprises. As a kind of service industry, distribution optimizes the vehicle scheduling problem, not only to optimize the cost of distribution enterprises, but also to ensure the efficiency of service and improve the customer satisfaction index. And then improve the competitiveness of distribution enterprises, so that they can get a long-term development in the fierce competition. In this paper, based on the related literature and research results, a vehicle scheduling problem model for customer satisfaction evaluation is established, and particle swarm optimization algorithm and improved particle swarm optimization algorithm are used to solve the model. To verify the effectiveness of the algorithm. The details are as follows: (1) Model building. In order to better evaluate customer satisfaction and make the model closer to reality, this paper introduces trapezoidal fuzzy time function. The customer satisfaction is evaluated by the expected service time and the allowable service time, and the cost and time factors are taken into account. Multi-objective model with minimum time and cost. (2) algorithm. Firstly, the algorithm for vehicle scheduling problem is studied in detail, and the advantages and disadvantages of different algorithms are explained by comparing and analyzing the algorithm by solving an example. Then, aiming at the defects of the standard particle swarm algorithm, the replication and migration operators in the colony algorithm are introduced and improved, and the standard test function is used to verify the algorithm. Compared with the standard PSO, the improved PSO has a stronger searching ability and a certain ability to jump out of the local optimum. Finally, in order to better use the improved particle swarm optimization algorithm to solve the model, the encoding and evolution of particles are improved, and the selection of weight scheme is discussed. It is proved that the improved particle swarm optimization algorithm is effective in solving vehicle scheduling problems.
【学位授予单位】：兰州交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F252

【参考文献】