基于自适应遗传算法的高速公路服务区物流配送中心选址研究

发布时间：2018-11-04 08:02

【摘要】：高速公路服务区作为高速公路上的重要组成部分,伴随着高速公路路网的快速发展,其本身对于配送的需求已具有一定的规模。由于高速公路特有的封闭性限制了车辆的出入,离供应商的距离较远从而导致过长的配送路线等原因,制约了服务区的物流配送水平,阻碍了高速公路服务区企业的快速发展。根据服务区的自身需求,建立共同配送中心并进行科学合理的选址决策,不仅能够提高服务区的物流配送效率,而且将为高速公路服务区进一步转型发展及优化服务区整体配送网络提供助力。本文首先总结了之前学者关于高速公路服务区拓展物流配送的研究,并阐述了相关的基础理论。在基于对服务区的配送模式的选择探讨之后,阐述了高速公路服务区发展共同配送的重要意义。接下来从模型的建立和实证分析两个方面来进行研究探讨,以建立服务区共同配送中心为目标,从更切合实际的角度来构建了考虑多供应商多商品的配送成本、库存成本和建造成本的的多配送中心选址模型。针对之前建立的模型,利用Python来进行编程。在算法方面,首先采用Dijkstra算法对服务区之间以及供应商到服务区之间的最短路进行求解;因为传统遗传算法容易陷入局部最优解,本文采用自适应遗传算法求解最终的选址结果。实证分析部分,选取LN省的65对高速公路服务区作为实证分析的样本,并相应的给出了LN省服务区共同配送中心的选址结果,以及供应商的选择和所配送服务区的分配,确定了所选配送中心的经济订货批量,验证了模型和算法选择的有效性。本文的结论:在建设模型时假设服务区对商品的日需求量服从正态分布,而在后期验证模型是否可行时,运用SPSS软件对需求量数据是否满正态分布给予了验证,并确定数据服从正态分布;采用自适应算法对模型进行求解后,最终选定3号、6号、38号、44号、51号作为LN省的高速公路服务区配送中心的选址地点。
[Abstract]:As an important part of expressway service area, with the rapid development of highway network, the demand for distribution has a certain scale. Because of the unique closure of freeway, which restricts the access of vehicles and the distance from suppliers, it leads to the long distribution route, which restricts the level of logistics distribution in service area and hinders the rapid development of enterprises in expressway service area. According to the demand of the service area, the establishment of the joint distribution center and the scientific and reasonable location decision can not only improve the efficiency of logistics distribution in the service area. Furthermore, it will help to develop and optimize the whole distribution network of expressway service area. Firstly, this paper summarizes the previous research on extending logistics distribution in expressway service area, and expounds the relevant basic theory. After discussing the choice of distribution mode of service area, this paper expounds the significance of developing joint distribution in expressway service area. Then from the establishment of the model and empirical analysis to study and discuss, to establish a service area distribution center as the goal, from a more realistic point of view to build a multi-supplier and multi-commodity distribution costs. Inventory cost and construction cost of multiple distribution center location model. According to the previous model, the use of Python to program. In the aspect of algorithm, firstly, Dijkstra algorithm is used to solve the shortest path between service areas and between suppliers and service areas. Because the traditional genetic algorithm is easy to fall into the local optimal solution, the adaptive genetic algorithm is used to solve the final location result. In the part of empirical analysis, 65 pairs of expressway service area in LN province are selected as the sample of the empirical analysis, and the location results of the joint distribution center of LN province service area are given, as well as the selection of suppliers and the distribution of the distribution service area. The economic order batch of the selected distribution center is determined, and the validity of the model and algorithm selection is verified. The conclusion of this paper is that the daily demand for goods in service area is normally distributed when the model is constructed, and when the model is feasible or not, the SPSS software is used to verify the full normal distribution of demand data. And determine the data from the normal distribution; After solving the model with adaptive algorithm, the location of distribution center of expressway service area in LN province is selected as No. 3, No. 6, No. 38, No. 44 and No. 51.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;F542.6;F252

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