本文選題：三維裝箱優(yōu)化　切入點(diǎn)：離線算法　出處：《吉林大學(xué)》2014年碩士論文

【摘要】：裝箱優(yōu)化問題廣泛應(yīng)用于工業(yè)制造、計(jì)算機(jī)技術(shù)以及日常生活中方面，對(duì)于三維離線裝箱問題而言，其最主要的應(yīng)用領(lǐng)域?yàn)樨涍\(yùn)輪渡船艙配載、貨運(yùn)列車車廂配載、集裝箱裝載、汽車車廂裝載等方面，跟物流行業(yè)聯(lián)系較為緊密，但是此類優(yōu)化問題求解過程較為復(fù)雜。在物流配送活動(dòng)中，裝箱效果的好壞對(duì)于配送成本將產(chǎn)生直接的影響，隨著物流行業(yè)的快速發(fā)展，學(xué)者們對(duì)于物流配送領(lǐng)域的箱體裝載研究日益深入。但是就目前情況而言，學(xué)者的研究都只能以某種算法得出其近似解，所設(shè)計(jì)算法的優(yōu)化結(jié)果以及時(shí)間性能都不夠理想，而市面上大多數(shù)裝箱軟件操作界面的用戶友好性較差，裝載效果也有待進(jìn)一步改進(jìn)。因此，針對(duì)三維離線裝箱優(yōu)化問題，設(shè)計(jì)高效快速的算法具有重要的理論和現(xiàn)實(shí)意義，不僅可以解決物流配送領(lǐng)域的貨物裝載問題，，也可以為后續(xù)裝箱優(yōu)化軟件的設(shè)計(jì)和開發(fā)以及其他相關(guān)問題的研究提供借鑒依據(jù)。 本文以三維離線裝箱問題為研究對(duì)象，利用遺傳算法和模擬退火算法集成的思路對(duì)該問題進(jìn)行求解，并編寫程序代碼在Matlab環(huán)境下進(jìn)行實(shí)現(xiàn)。 首先，本文對(duì)國內(nèi)外學(xué)者關(guān)于三維裝箱優(yōu)化問題的研究現(xiàn)狀進(jìn)行了總結(jié)，在此基礎(chǔ)上，確定了本文所研究的主要內(nèi)容以及采用的技術(shù)方法，并對(duì)三維裝箱問題的主要研究方法進(jìn)行了詳細(xì)闡述，包括遺傳算法、啟發(fā)式算法以及混合遺傳算法的主要優(yōu)缺點(diǎn)，另外，還對(duì)本文的主要技術(shù)方法之一——模擬退火算法進(jìn)行了詳細(xì)介紹。 其次，本文針對(duì)三維離線裝箱優(yōu)化問題的具體特點(diǎn)，提出了裝箱優(yōu)化模型具體的目標(biāo)函數(shù)和約束條件數(shù)值化處理方法，構(gòu)建出最終的數(shù)學(xué)模型，后續(xù)以該模型為導(dǎo)向，確定了本文所設(shè)計(jì)算法的基本要素以及遺傳模擬退火算法的集成方法，在該算法集成的框架下，利用Matlab進(jìn)行算法代碼編程和實(shí)現(xiàn)，并利用其他學(xué)者論文中的數(shù)據(jù)對(duì)算法的有效性進(jìn)行驗(yàn)證。 最后，本文以長春市某汽車公司為例，對(duì)該公司的供應(yīng)商配送數(shù)據(jù)進(jìn)行整理，選取五個(gè)供應(yīng)商為研究對(duì)象，利用前文所設(shè)計(jì)的算法進(jìn)行實(shí)際裝載，并將最終優(yōu)化結(jié)果與各個(gè)供應(yīng)商現(xiàn)有裝載數(shù)據(jù)進(jìn)行對(duì)比分析，進(jìn)一步論證了算法和程序代碼的有效性。
[Abstract]:The packing optimization problem is widely used in industrial manufacture, computer technology and daily life. For the three-dimensional off-line packing problem, the main application fields are cargo ferry cabin stowage, freight train compartment stowage.Container loading, car loading and other aspects are closely related to logistics industry, but the process of solving this optimization problem is more complex.In the logistics distribution activities, the effect of packing will have a direct impact on the cost of distribution. With the rapid development of logistics industry, scholars on the field of logistics distribution box loading research is increasingly in-depth.However, as far as the present situation is concerned, scholars can only get their approximate solution by some algorithm. The optimization result and time performance of the designed algorithm are not ideal, and the user friendliness of the operating interface of most boxed software on the market is poor.The loading effect also needs to be further improved.Therefore, it is of great theoretical and practical significance to design an efficient and fast algorithm for the optimization of 3D off-line packing, which can not only solve the cargo loading problem in the field of logistics distribution.It can also provide reference for the design and development of optimization software and other related issues.In this paper, the problem of three dimensional off-line packing is studied. The genetic algorithm and simulated annealing algorithm are integrated to solve the problem, and program code is written to realize it in Matlab environment.First of all, this paper summarizes the domestic and foreign scholars' research status of three-dimensional packing optimization problem, and on this basis, determines the main contents of this study and the technical methods used in this paper.The main research methods of 3D packing problem are described in detail, including the main advantages and disadvantages of genetic algorithm, heuristic algorithm and hybrid genetic algorithm.The simulated annealing algorithm, one of the main techniques in this paper, is also introduced in detail.Secondly, according to the specific characteristics of the three-dimensional off-line packing optimization problem, this paper puts forward the concrete objective function and the numerical treatment method of the constraint conditions of the packing optimization model, and constructs the final mathematical model, which is followed by the model.The basic elements of the algorithm designed in this paper and the integration method of genetic simulated annealing algorithm are determined. Under the framework of the algorithm integration, the algorithm code is programmed and implemented by using Matlab.The validity of the algorithm is verified by using the data of other scholars.Finally, taking an automobile company in Changchun as an example, this paper collates the supplier distribution data of the company, selects five suppliers as the research object, and uses the algorithm designed above to carry out actual loading.Finally, the optimization results are compared with the existing loading data of each supplier, and the validity of the algorithm and program code is further demonstrated.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP18;U116

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