汽车零部件物流中心三维装箱问题研究
发布时间：2018-07-25 17:52
【摘要】：随着国内市场经济的快速发展,汽车行业开始进入爆发式的增长阶段。汽车零部件物流中心作为汽车制造商和售后服务商的重要仓储物流中心,物流中心的配送规模与频率越来越大。作为物流配送中极其重要的终端环节,汽车零部件物流中心的装箱问题受到越来越多的重视。运用科学的方法对装箱问题进行最优分配,提高运输车辆装载率,满足不同情况下物品的配送需求,对于降低企业运营成本以及满足客户的需求具有重要意义。本文以汽车零部件物流中心为研究对象,主要研究零部件出库配送时的装箱和装车问题。首先介绍了汽车零部件物流中心的功能及其整体作业流程,并深入地分析了汽车零部件物流中心的装箱作业流程,分别对各个阶段的包装箱装箱问题、托盘装载问题以及装车问题的进行了详细的分析,总结出各阶段问题求解的目标函数和约束条件。然后,本文建立了考虑多目的地配送的三维装箱问题的多目标优化模型,同时,模型能够满足装载结构的可调特性,同时设计了其混合量子遗传算法。本模型的优化目标为装载空间利用率最大与物品加权重心与空间中轴便宜度最小,使得装载结构能够满足企业实际运营需求。本文设计的混合量子遗传算法以量子遗传算法为基础,引入基于平面的空间描述策略作为解码策略。为此,提出一种新的量子编码方式、修正策略以及量子门更新机制。与传统进化算法相比,量子遗传算法具有更强的全局遍历能力和不易陷入局部最优解的能力。最后,设计系统并实现算法,分别对装箱问题的装载结构可调性、多目的地配送的装车问题以及包含托盘装载二次装载问题进行实例测试,实例测试和比较分析表明,本算法的优化效果好、装载率高,物品装载结构稳定并且具有可调整特性,可有效满足各种情况下的装箱问题,对企业实际运营具有较强的指导意义。
[Abstract]:With the rapid development of the domestic market economy, the automobile industry begins to enter the explosive growth stage. As an important storage and logistics center for automobile manufacturers and after-sales service companies, the distribution scale and frequency of automobile parts logistics center is becoming larger and larger. As an important terminal in logistics distribution, more and more attention has been paid to the packing of automobile parts logistics center. It is of great significance to use scientific method to optimize the distribution of the packing problem, to improve the loading rate of the transport vehicles and to meet the distribution demand of the goods under different conditions. It is of great significance to reduce the operating cost of the enterprise and to meet the needs of the customers. In this paper, the automobile parts logistics center as the research object, the main research parts out of storage distribution of the packing and loading problems. Firstly, this paper introduces the function of automobile parts logistics center and its whole operation flow, and analyzes the packing operation flow of automobile parts logistics center. The pallet loading problem and the loading problem are analyzed in detail, and the objective function and constraint conditions for solving each stage problem are summarized. Then, a multi-objective optimization model considering multi-destination distribution is established, and the model can satisfy the adjustable characteristics of loading structure, and its hybrid quantum genetic algorithm is designed at the same time. The optimization goal of this model is to maximize the utilization rate of loading space and to minimize the weight center of gravity and the cheapness of the axis in the space, so that the loading structure can meet the actual operational requirements of the enterprise. The hybrid quantum genetic algorithm designed in this paper is based on quantum genetic algorithm (QGA), and a planar spatial description strategy is introduced as the decoding strategy. Therefore, a new quantum coding scheme, a modification strategy and a quantum gate updating mechanism are proposed. Compared with the traditional evolutionary algorithm, quantum genetic algorithm (QGA) has stronger global ergodic ability and less ability to fall into local optimal solution. Finally, the system is designed and the algorithm is implemented to test the loading structure of the packing problem, the loading problem of multi-destination distribution and the secondary loading problem including pallet loading, respectively. The example test and comparative analysis show that, The algorithm has good optimization effect, high loading rate, stable loading structure and adjustable property. It can effectively meet the packing problem under various circumstances and has a strong guiding significance for the actual operation of enterprises.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F426.471;F252
本文编号：2144580
[Abstract]:With the rapid development of the domestic market economy, the automobile industry begins to enter the explosive growth stage. As an important storage and logistics center for automobile manufacturers and after-sales service companies, the distribution scale and frequency of automobile parts logistics center is becoming larger and larger. As an important terminal in logistics distribution, more and more attention has been paid to the packing of automobile parts logistics center. It is of great significance to use scientific method to optimize the distribution of the packing problem, to improve the loading rate of the transport vehicles and to meet the distribution demand of the goods under different conditions. It is of great significance to reduce the operating cost of the enterprise and to meet the needs of the customers. In this paper, the automobile parts logistics center as the research object, the main research parts out of storage distribution of the packing and loading problems. Firstly, this paper introduces the function of automobile parts logistics center and its whole operation flow, and analyzes the packing operation flow of automobile parts logistics center. The pallet loading problem and the loading problem are analyzed in detail, and the objective function and constraint conditions for solving each stage problem are summarized. Then, a multi-objective optimization model considering multi-destination distribution is established, and the model can satisfy the adjustable characteristics of loading structure, and its hybrid quantum genetic algorithm is designed at the same time. The optimization goal of this model is to maximize the utilization rate of loading space and to minimize the weight center of gravity and the cheapness of the axis in the space, so that the loading structure can meet the actual operational requirements of the enterprise. The hybrid quantum genetic algorithm designed in this paper is based on quantum genetic algorithm (QGA), and a planar spatial description strategy is introduced as the decoding strategy. Therefore, a new quantum coding scheme, a modification strategy and a quantum gate updating mechanism are proposed. Compared with the traditional evolutionary algorithm, quantum genetic algorithm (QGA) has stronger global ergodic ability and less ability to fall into local optimal solution. Finally, the system is designed and the algorithm is implemented to test the loading structure of the packing problem, the loading problem of multi-destination distribution and the secondary loading problem including pallet loading, respectively. The example test and comparative analysis show that, The algorithm has good optimization effect, high loading rate, stable loading structure and adjustable property. It can effectively meet the packing problem under various circumstances and has a strong guiding significance for the actual operation of enterprises.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F426.471;F252
【参考文献】
相关期刊论文 前1条
1 吴楚楠;刘科峰;彭斯俊;黄樟灿;;大规模集装箱装载问题[J];计算机工程与应用;2013年01期
，本文编号：2144580
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