臺灣博碩士論文加值系統

對於企業在計畫生產過程中，倉庫管理是很重要的一部分。因此，存貨的分類是必要的，每一份存庫類是要靠它的屬性，特徵來相應安排。為了有效地分類大量庫存物品和多標準，應用理想值接近法TOPSIS（技術優先順序通過逼近理想解法）模型和VIKOR為ABC分析來分析庫存物品，以庫存物品分為不同的類別A，B和C。傳統上，標準分類僅是根據每年的使用美金。此技術考慮到不同的評估屬性，如交貨時間，單位成本，與採購量。此方法應用層級分析法（AHP）來計算際每個屬性的權重。最後，利用一製造彩色液晶顯示器（LCD）零組件存貨之實際例子，示範此方法如何應用。

Inventory management is an important part of production planning process for many enterprises. Therefore, inventory classification is necessary so that each inventory class can be treated accordingly based on the attributes of that class. In order to effectively classify with large amount of inventory items and multi-criteria, using closeness to the ideal approach TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model and VIKOR for ABC analysis of inventory items to classify inventory items into different categories A, B and C. Traditionally, the classification criteria is only based on annual dollar usage. The technique takes into consideration various criteria such as lead time, unit cost, purchaser volume. This approach utilizes AHP (Analytic Hierarchy Process) have consistency consideration in determine the weights of each criterion. Finally, a real life example regarding the inventory items used for color LCD monitor manufacturing is presented to demonstrate how the proposed method can be applied.

CONTENTS

Title i

Contents iii

List of Figures v

List of Tables vi

Abstract vii

摘要 viii

CHAPTER 1: INTRODUCTION 1

1.1 Research Background 1

1.2 Research Motivation 4

1.3 Research Objectives 4

1.4 Research Procedures 5

1.5 Research Organization 6

CHAPTER 2: LITERATURE REVIEW 7

2.1 Review of AHP Methodology 7

2.2 Review of TOPSIS 8

2.3 Review of VIKOR 10

CHAPTER 3: RESEARCH METHODOLOGY 11

3.1 Research Framework 11

3.2 Criteria Selection 14

3.3 AHP method 14

3.4 The Expert choice software 16

3.5 TOPSIS Method 16

3.6 VIKOR Method 18

CHAPTER 4: A NUMERIC EXAMPLE 19

4.1 Hierarchical structure 19

4.2 Calculate the weights of criteria 20

4.3 TOPSIS method 22

4.4 VIKOR Method 34

CHAPTER 5: CONCLUSIONS AND SUGGESTIONS 41

3

5.1 Research Conclusion 41

5.2 Managerial Implication 42

5.3 Directions for Future Research 43

REFERENCES 44

4

LIST OF FIGURES

Figure 1.1: Classification sample for three categories 2

Figure 1.2: Diagram of thesis procedures 6

Figure 3.1: Research framework 13

Figure 3.2: Hierarchical Structure for AHP (Saaty, 1977 and 1994) 14

Figure 4.1: Hierarchical structure 20

5

LIST OF TABLES

Table 2.1: Some applications of TOPSIS 9

Table 3.1: Nine-point intensity of importance scale and its description. 15

Table 4.1: For expert D1 pairwise comparison matrix 21

Table 4.2: For expert D2 pairwise comparison matrix 21

Table 4.3: For expert D3 pairwise comparison matrix 21

Table 4.4: For expert D4 pairwise comparison matrix 21

Table 4.5: For expert D5 pairwise comparison matrix 22

Table 4.6: Weight of criteria 22

Table 4.7: Original data decision-making matrix 22

Table 4.8: Normalization decision matrix 25

Table 4.9: Weighted normalized matrix 27

Table 4.10: Distances of each alternative from the ideal solutions 29

Table 4.11: Distances of each alternative from negative-ideal solutions 30

Table 4.12: Result classification under multi-criteria by TOPSIS and Traditional ABC 32

Table 4.13: The best value and the worst value of all the criteria 34

Table 4.14: S and R interval numbers 35

Table 4.15: Q interval numbers 36

Table 4.16: Ranking of alternatives via Si, Ri, Qj. 38

Table 4.17: Comparison result of TOPSIS and VIKOR method 39

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