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研究生:賴鍵元
研究生(外文):Chien-Yuan Lai
論文名稱:軟性計算於商品管理之應用
論文名稱(外文):Application of soft computing to the merchandise management
指導教授:張百棧張百棧引用關係
學位類別:博士
校院名稱:元智大學
系所名稱:工業工程與管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
畢業學年度:94
語文別:英文
論文頁數:127
中文關鍵詞:軟性計算商品管理自組織映射圖網路案例式推理法基因演算法
外文關鍵詞:Soft ComputingMerchandise managementSelf-organizing MapsCase-based ReasoningGenetic Algorithm
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隨著世界潮流與消費型態的轉變,企業面臨比以往更嚴苛的挑戰,微利時代的來臨,已對企業產生重大的影響,此時,企業如何在微利時代與瞬息萬變的顧客需求競賽中,快速回應消費者的需求與強化企業的競爭優勢,商品管理便扮演重要的角色。為了達到有效的商品管理,事前的預測工作便扮演著決定性的因素。由於預測本身具有複雜與非結構性等不確定性因素,因此,本論文提出相關之軟性計算(Soft Computing)技術來輔助管理者應用於商品管理之決策上。
為了驗證本研究所提出之方法具有優異的預測能力,藉由實務之圖書批發商為案例,針對其新產品銷售與滯銷商品退貨兩大商品管理議題,分別提出結合自組織映射圖網路(SOM)與案例式推理CBR法之新書銷售預測方法,與混合基因演算法(GA)與案例式推理(CBR)之滯銷商品退貨預測方法,經實驗結果顯示,本研究所提出之混合方法在各項預測的準確度上比其他方法具有更為優異之預測能力,可有效提升商品管理之績效,以強化企業競爭之優勢。綜合本研究之成果,可以瞭解將軟性計算之技術應用在商品管理問題上是相當有潛力的。
Merchandise management plays a crucial role for successful enterprises. Forecast becomes critical when strengthening the functions of merchandise management. As forecasting has in itself uncertain factors like complexity and being unstructured, the present thesis proposed related Soft Computing techniques to provide managers with aid in the applications to decision making. In order to demonstrate that the methods proposed in this research stand out in forecast capability, book wholesalers are used as a case, with their sales of new products and returns of slow-selling goods as two major subjects in merchandise management. Presented, respectively, to deal with these two subjects are the forecasting method for sales of new released books combining Self-organizing Maps (SOM) with Case-based Reasoning (CBR) method, and the forecasting method for returns of slow-selling books blending the Genetic Algorithm (GA) with the CBR method. The experimental results demonstrated that these hybrid methods this research proposed outperformed other methods in various forecasting in terms of accuracies. With the achievements of this research summed up, it is known that the soft computing will have notable potentials to be applied to problems in the merchandise management.
Contents

Abstract in Chinese ……………………………………………………………..…….. i
Abstract in English …………………………………………………….………..……. ii
Acknowledgement …………………………………………………….………..…...... iii
Contents …………………………………………………………..……………..……. iv
List of Tables …………………………………………………………………………. vii
List of Figures ……………………….………………………………………..……… ix
1. Introduction
1.1 Background of the Research ……………………………………………….. 1
1.2 Motives of the Research ………………………………………………........ 2
1.3 Objective of the Research ………………………………………………….. 4
1.4 Framework of the Research ………………………………………………... 7
2. Literature Review on Soft Computing
2.1 Self-Organizing Maps ………………………………….…………………... 9
2.2 Genetic Algorithm ………………………………………………………… 14
2.3 Case-based Reasoning ……………………………………………………. 19
3. Sales Management:
Application of soft computing to the sales forecast of new products
3.1 Background …………………………………………………….................. 26
3.2 Problem description ………………………………………………………. 34
3.3 Methodology …..………………………………………………………….. 39
3.3.1 Self-organizing maps ….……………………………………..…… 40
3.3.2 Case-based Reasoning ……………………………………………. 45
3.3.3 Performances Evaluation………………………………..………… 48
3.4 Experimental results …………………………………..………………….. 48
3.4.1 Data description …………………………………..………………. 48
3.4.2 Data analysis ……………………………………………………… 49
3.4.3 Data standardization ......................................................................... 51
3.4.4 Test of independence for factors ………………………………...... 54
3.4.5 Cluster Analysis on the book case-base ........................................... 55
3.4.5.1 Clustering Analysis and Results on SOM method
Training Samples ……….................................................. 55
3.4.5.2 ANOVA on clustering result by SOM method ………..... 58
3.4.5.3 Scheffe''s Multiple Comparison on clustering
results by SOM method …………………………..…….. 59
3.4.5.4 Analysis on clustering results of training samples
by K-mean method ..………………………………......... 60
3.4.5.5 A case example for CBR with clusters ..……………....... 61
3.4.6 Analysis on the forecasts for sales of new released books ……...... 62
3.4.6.1 Comparison of MAPE values of SOM/CBR,
K/CBR and Conventional CBR ………............................ 62
3.4.6.2 Analysis of forecast error grade of SOM/CBR,
K/CBR and Conventional CBR ………………………… 66
3.4.6.3 Logistics cost analysis on the sales forecast for
new released books ……………………………………... 67
3.5 Summary ……………………………..…………………………………… 69
4. Returns Management:
Application of soft computing to the returns forecast of slow-selling goods
4.1 Background ……………………………………………………………….. 71
4.2 Problem description ………………………………………………….….... 76
4.3 Methodology ……………………………………………………………… 80
4.3.1 Genetic algorithm. ………………………………………………... 81
4.3.2 A hybrid system combining GA and CBR ………………………... 89
4.3.3 Performances Evaluation …………………………………………. 91
4.4 Experimental results ……………………………………………………..... 92
4.4.1 Data description …………………………………………………... 92
4.4.2 Data analysis ……………………………………………………… 94
4.4.3 Data standardization ……………………………….……………… 95
4.4.4 A case example for different forecasting models ..………………... 97
4.4.5 Results and Analysis …………………………………………….. 103
4.4.5.1 Related parameters designed for
the Hybrid System ……………………………............... 103
4.4.5.2 Related parameters designed for BPNN ………………. 104
4.4.5.3 Comparison and analysis of average error rate
of each model ………………………………………….. 107
4.5 Summary ……………………………………………………………..….. 112
5. Conclusions and Future Works
5.1 Conclusions …………………………………………………………..….. 114
5.2 Future Works…………………..……………….………………………… 115
Reference ……………………………………………………………………………. 117

List of Tables

Table 3.1 The level of computerization of single bookstore and chain store ……… 32
Table 3.2 Factors affecting the sales of new books ……………………………....... 51
Table 3.3 Standardized values for feature factors after transformation …………… 53
Table 3.4 Test of independence for each factor and average sales volumes ………. 54
Table 3.5 SOM values and number of cases of training samples
in three groups ……………………………………………………........... 57
Table 3.6 Statistics of training samples of clustering result ……………………….. 58
Table 3.7 ANOVA table for clustered SOM ………………………………………. 58
Table 3.8 Analysis by Scheffe''s Multiple Comparison ……………………………. 59
Table 3.9 Statistics of clustering by SOM method ……………................................ 60
Table 3.10 Statistics of clustering by K-mean method ………………….………….. 61
Table 3.11 MAPE values for various methods and various k ………………………. 64
Table 3.12 Definition table of forecast error grades ………………………………... 66
Table 3.13 Analysis of forecast error grades under different methods ....................... 67
Table 3.14 Comparison on total cost in various methods …………………………... 68
Table 3.15 Comparison on logistics cost in various methods ………………………. 68
Table 4.1 Sample return percentages ……………………………………………… 73
Table 4.2 Reasons for returns …………………………………................................ 74
Table 4.4 Averaged returns rate (including new and old books)
by book retailers ………………………………………………………… 76
Table 4.5 Description of notations ………………………………………………… 86
Table 4.6 Calculation of objective function for a set of training cases ……………. 86
Table 4.7 Definition of selected factors …………………………………………… 95
Table 4.8 Standardized data samples ……………………………………………… 96
Table 4.9 Sample data for reference cases ………………………………………… 97
Table 4.10 Sample data for training cases ………………………………………….. 97
Table 4.11 Sample data for testing cases …………………………………………… 99
Table 4.12 e value and average error rate (Model A) …………………………….. .100
Table 4.13 e value and average error rate (Model B) …………………………....... 101
Table 4.14 e value and average error rate (Model C) …………………………....... 101
Table 4.15 e value and average error rate (Model D) ............................................... 102
Table 4.16 Signal levels and codes of factors (Model A) …………………………. 104
Table 4.17 S/N ratio (Model A) …………………………………………………… 104
Table 4.18 Signal levels and codes of factors (Model B) …………………………. 105
Table 4.19 S/N ratio (Model B) …………………………………………………… 106
Table 4.20 Average error rates for Model A under different reference cases …….. 107
Table 4.21 Average error rates and Standard Deviations under
200 training cases and 100 testing cases for Model A, B, C and D …… 108
Table 4.22 Average error rates for Model A under different reference cases …….. 108
Table 4.23 Average error rates and Standard Deviations under
200 training cases and 200 testing cases for Model A, B, C and D …… 108
Table 4.24 Average error rates for Model A under different reference cases …….. 109
Table 4.25 Average error rates and Standard Deviations under
300 training cases and 100 testing cases for Model A, B, C and D …… 109



List of Figures

Figure 1.1 Illustration of the research framework …………………………………… 8
Figure 2.1 The concept of SOM space reflection ………………………………....... 11
Figure 2.2 SOM algorithm ………………………………………………………….. 12
Figure 2.3 Genetic algorithm ………………..……………………………………… 15
Figure 2.4 Flow chart of genetic algorithm .……………………………………....... 16
Figure 2.5 CBR cycle ………………………………………………………………. 21
Figure 3.1 Statistics in number of publishers ………………………………………. 27
Figure 3.2 Statistics of titles published …………………………………………….. 28
Figure 3.3 Sales channels in book publishing industry …………………………...... 29
Figure 3.4 The framework of the hybrid system combining SOM and CBR ............. 40
Figure 3.5 Diagram of SOM network framework ………………………………...... 41
Figure 3.6 Diagram of case colleting period ……………………………………...... 49
Figure 3.7 Convergence diagram of RMS learning times of SOM method ............... 56
Figure 3.8 Scatter diagram for SOM output values of training samples …………… 56
Figure 3.9 Results of clustering by SOM method …………………..……………… 57
Figure 3.10 The example of CBR with cluster………………………………..……… 62
Figure 3.11 MAPE values for various methods and various k ………………………. 65
Figure 3.12 Variations of MAPE values at various values for k …………………….. 65
Figure 4.1 The framework of the hybrid system combining GA and CBR ………… 81
Figure 4.2 Learning architecture of case factor weights …….…………………....... 84
Figure 4.3 Illustration of Encoding …………………………………………………. 85
Figure 4.4 Two-point crossover …………………………………………….………. 88
Figure 4.5 Two-point mutation …………………………………………….……….. 89
Figure 4.6 Groups of different cases for different models ………………….………. 93
Figure 4.7 Illustration for data collecting period …………………………………… 93
Figure 4.8 Learning times and average error rate ..................................................... 106
Figure 4.9 Average error rate of four different models under
different testing conditions …………………………………………….. 110
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