战时物流配送车辆路径优化问题研究

发布时间：2018-04-14 05:24

本文选题：战时 + 物流配送　；参考：《国防科学技术大学》2015年硕士论文

【摘要】：隆美尔曾说过,“战斗在第一枪打响之前是由军需官决定的。”阿富汗战争、海湾战争、伊拉克战争、利比亚战争等一系列战争实践表明,随着战争信息化程度的不断加深,其对后勤补给的依赖也更加明显。物流配送是保障物资交付作战部队的最后一环,也是最为重要、最为困难、最为危险的一环。本文基于军事物流的需求背景开展战时物流配送车辆路径优化问题研究,主要的工作和成果如下:1.在介绍了我国物流业总体发展情况的基础上,分析车辆路径问题的理论价值和现实意义。以美军实际作战行动为例阐述了军事物流在后勤保障中的重要作用,从而进一步论述了战时车辆路径优化问题的重要性。对车辆路径问题的国内外研究现状进行了综述。2.从我军军事物流发展现状、战时物流配送的特点入手,对战时物流配送车辆路径优化问题的主要影响因素展开分析。3.在对车辆路径问题基本模型和常用算法进行简要分析的基础上,将蚁群算法中信息素更新方式进行了改进。通过与文献中算例结果的对比分析,表明本文改进算法一方面提高了计算效率,另一方面能够获得更优解。此外,采用Solomon标准测试数据中的C1、C2类数据进行了检验,在C1类9组数据中,本文算法得出的7条优化路线达到了用启发式算法得出的最知名解(Best Known Solutions Identified by Heuristics),其余2组数据的优化结果也非常接近该最知名解,在C2类8组数据中,本文算法得出的8条优化路线全部达到了用启发式算法得出的最知名解,进一步证明了本文改进算法的有效性。4.在建立了带硬时间窗的蚁群算法模型的基础上,针对战时物流配送过程中敌方火力打击对运输线路的影响、对运输物资造成的损失等因素改进了算法模型。将算法应用于考虑路面毁伤、物资损耗的战时物流配送问题,得出了静态最优路线,并通过仿真计算检验了线路的合理性。又进一步贴近战场实际,将作战单元对物资的需求变化、战场路况变化纳入模型之中,通过对算法的适当调整,使得模型能够解决动态问题,计算出了最优路线,并通过仿真实验检验了算法的合理性和有效性。
[Abstract]:Rommel once said, "the battle was decided by the quartermaster before the first shot was fired."A series of war practices such as Afghanistan, Gulf, Iraq and Libya show that with the deepening of war informatization, its dependence on logistics supplies is more obvious.Logistics distribution is the last link to ensure the delivery of materials to combat troops, is also the most important, the most difficult, the most dangerous link.Based on the demand background of military logistics, this paper studies the vehicle routing optimization in wartime logistics distribution. The main work and results are as follows: 1.Based on the introduction of the general development of the logistics industry in China, the theoretical value and practical significance of the vehicle routing problem are analyzed.Taking the actual operations of the US military as an example, this paper expounds the important role of military logistics in logistics support, and further discusses the importance of vehicle routing optimization in wartime.The research status of vehicle routing problem at home and abroad is summarized.Based on the current situation of military logistics development and the characteristics of wartime logistics distribution, this paper analyzes the main influencing factors of vehicle routing optimization in wartime logistics distribution.Based on the analysis of the basic model and common algorithms of vehicle routing problem, the pheromone updating method in ant colony algorithm is improved.By comparing with the results of the numerical examples in the literature, it is shown that the improved algorithm can improve the computational efficiency and obtain a better solution on the other hand.In addition, the C _ 1C _ 2 data from Solomon standard test data are used to test, and in C _ 1 class 9 groups of data,The seven optimization routes obtained by this algorithm reach the best Known Solutions Identified by heuristic algorithm, and the optimization results of the other two groups of data are very close to the best known solution, in the C2 class of 8 groups of data.All of the 8 optimized routes obtained by this algorithm reach the best known solution obtained by heuristic algorithm, which further proves the effectiveness of the improved algorithm in this paper. 4.Based on the ant colony algorithm model with hard time window, the algorithm model is improved in view of the influence of enemy firepower attack on transportation route and the loss of transportation materials in wartime logistics distribution process.The algorithm is applied to the wartime logistics distribution problem considering road damage and material loss, and the static optimal route is obtained, and the rationality of the route is verified by simulation calculation.Further closer to the reality of the battlefield, the requirements of combat units for material changes, battlefield changes in road conditions into the model, through appropriate adjustment of the algorithm, the model can solve dynamic problems and calculate the optimal route.The rationality and validity of the algorithm are verified by simulation experiments.
【学位授予单位】：国防科学技术大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：E144;U116.2

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