汽車零部件物流中心三維裝箱問題研究
發(fā)布時間：2018-07-25 17:52
【摘要】：隨著國內(nèi)市場經(jīng)濟的快速發(fā)展,汽車行業(yè)開始進入爆發(fā)式的增長階段。汽車零部件物流中心作為汽車制造商和售后服務(wù)商的重要倉儲物流中心,物流中心的配送規(guī)模與頻率越來越大。作為物流配送中極其重要的終端環(huán)節(jié),汽車零部件物流中心的裝箱問題受到越來越多的重視。運用科學(xué)的方法對裝箱問題進行最優(yōu)分配,提高運輸車輛裝載率,滿足不同情況下物品的配送需求,對于降低企業(yè)運營成本以及滿足客戶的需求具有重要意義。本文以汽車零部件物流中心為研究對象,主要研究零部件出庫配送時的裝箱和裝車問題。首先介紹了汽車零部件物流中心的功能及其整體作業(yè)流程,并深入地分析了汽車零部件物流中心的裝箱作業(yè)流程,分別對各個階段的包裝箱裝箱問題、托盤裝載問題以及裝車問題的進行了詳細(xì)的分析,總結(jié)出各階段問題求解的目標(biāo)函數(shù)和約束條件。然后,本文建立了考慮多目的地配送的三維裝箱問題的多目標(biāo)優(yōu)化模型,同時,模型能夠滿足裝載結(jié)構(gòu)的可調(diào)特性,同時設(shè)計了其混合量子遺傳算法。本模型的優(yōu)化目標(biāo)為裝載空間利用率最大與物品加權(quán)重心與空間中軸便宜度最小,使得裝載結(jié)構(gòu)能夠滿足企業(yè)實際運營需求。本文設(shè)計的混合量子遺傳算法以量子遺傳算法為基礎(chǔ),引入基于平面的空間描述策略作為解碼策略。為此,提出一種新的量子編碼方式、修正策略以及量子門更新機制。與傳統(tǒng)進化算法相比,量子遺傳算法具有更強的全局遍歷能力和不易陷入局部最優(yōu)解的能力。最后,設(shè)計系統(tǒng)并實現(xiàn)算法,分別對裝箱問題的裝載結(jié)構(gòu)可調(diào)性、多目的地配送的裝車問題以及包含托盤裝載二次裝載問題進行實例測試,實例測試和比較分析表明,本算法的優(yōu)化效果好、裝載率高,物品裝載結(jié)構(gòu)穩(wěn)定并且具有可調(diào)整特性,可有效滿足各種情況下的裝箱問題,對企業(yè)實際運營具有較強的指導(dǎo)意義。
[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.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F426.471;F252
本文編號：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.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F426.471;F252
【參考文獻】
相關(guān)期刊論文 前1條
1 吳楚楠;劉科峰;彭斯俊;黃樟燦;;大規(guī)模集裝箱裝載問題[J];計算機工程與應(yīng)用;2013年01期
，本文編號：2144580
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