結構式估計之大型混整數模型於班德氏基底切割生成規畫之應用與數值實驗

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研究生：	劉軼倫 Liu, Yi-Lun
論文名稱：	結構式估計之大型混整數模型於班德氏基底切割生成規畫之應用與數值實驗 The application of Benders-based cut generation program to a large-scale mixed-integer model of structural estimation with numerical experiments
指導教授：	李雨青 Lee, Yu-Ching
口試委員:	陳柏安 Chen, Po-An 郭佳瑋 Kuo, Chia-Wei
學位類別：	碩士 Master
系所名稱：	工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management
論文出版年：	2020
畢業學年度：	108
語文別：	英文
論文頁數：	50
中文關鍵詞：	純特徵模型、大型混整數規劃、班德氏分解法、切割生成規劃
外文關鍵詞：	pure characteristic model, large-scale mixed-integer program, Benders Decomposition, cut generation program
相關次數：	點閱：3 下載：0
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本篇論文將2001年至2015年英國汽車市場的銷售情況，結合純特徵模型(pure characteristic model)，並且配合事前所蒐集的正規化資料，將其模擬成一大型混整數規劃問題。由於此模型具有大量二元變數(binary variable)，因此在使用班德氏分解法(Benders Decomposition)對於該模型求解的情況下，我們試圖使用切割生成規劃(cut generation program)克服含有二元變數的子問題難以轉成對偶的困難點。過程中包含鬆弛變數(slack variable)的引入，放鬆(relaxation)、對偶轉換、互補差額性質等等，除此之外，利用變數代換以及額外限制式導入的技巧，我們可以將原本為非線性的切割生成規劃轉為線性，使求解該模型的難度大為降低。而在實際資料的數值實驗後也發現，在我們的原模型維度到達(N, J, K) = (34, 32, 32)以上後，透過求解我們的演算法之效率會開始比直接求解原模型來得好，而加快的比例逐漸上升，當維度為(N, J, K) = (99, 97, 32)時甚至來到大約10倍快，並且在RMSE上也有好的表現，此外該演算法也適用於生成資料。我們也冀望往後可以在該演算法上做進階的調整，例如限制式的分配、雙線性項(bilinear term)的處理等，來使更複雜的模型可以有更加有效率的求解方式。

In this thesis, the sales of automobile in British from 2001 to 2015 is studied. By combining pure characteristics model and normalized data, we can formulate a large-scale mixed-integer program. Since this model contains a large number of binary variables, we try to use cut generation program to overcome the difficulty of dual-transformation of a sub-problem with binary variables while using Benders Decomposition to solve the model. The process includes introduction of slack variables, relaxation, dual transformation, complementary slackness properties, etc. Moreover, variable substitution and introduction of constraints can turn a non-linear cut generation program into linear one and thus the difficulty to solve will be reduced extremely. After conducting numerical experiments with real data, we find that while the dimension of original model reaches (N, J, K) = (34, 32, 32), the efficiency of our algorithm will perform better than solving it directly. The rate of acceleration gradually increases. When the dimension is (N, J, K) = (99, 97, 32), the speed of our algorithm is about 10 times faster and also shows good results in RMSE. Furthermore, synthetic data can also be used in the algorithm. We also hope that in the future, some advanced adjustments to this algorithm can be made, such as constraints allocation, bilinear terms processing, etc., so that a more complex model can be solved more efficiently.

摘要    i
Abstract    ii
List of Tables    v
Chapter 1  Introduction    1
Chapter 2  Literature Review    4
2.1  BLP model    4
2.2  Classical Benders Decomposition    5
2.3  Benders Decomposition for Mixed-Integer Problem    9
2.4  Performance of MPEC on BLP Model    10
Chapter 3  Original Model    12
3.1  Data Description    12
3.2  Variables and Constraints Description    12
3.3  Objective Function Description    14
3.4  Complete Model    15
Chapter 4  Benders Decomposition with CGP    17
4.1  Define Master Problem and Sub-Problem    17
4.2  Introduction of Slack Variables    18
4.3  Sub-Problem Relaxation    19
4.4  Dual Sub-Problem and Complementary Slackness    20
4.5  Complete CGP Model    21
4.6  Linearization of CGP    25
4.7  Benders Cuts Addition    30
4.7.1  First Way To Add Benders Cuts    31
4.7.2  Second Way To Add Benders Cuts    31
4.7.3  Third Way To Add Benders Cuts    31
4.8  Full Algorithm of Benders Decomposition with CGP    33
Chapter 5  Numerical Experiments    35
Chapter 6  Conclusions    41
References    44
Appendix    48

                                

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