
系統識別號 
U00022706201722164500 
中文論文名稱

建構整合策略及戰術層級供應鏈網路設計之研究 
英文論文名稱

Construction of an integration supply chain network design with the strategic and tactical planning 
校院名稱 
淡江大學 
系所名稱(中) 
管理科學學系博士班 
系所名稱(英) 
Doctoral Program, Department of Management Sciences 
學年度 
105 
學期 
2 
出版年 
106 
研究生中文姓名 
何衛中 
研究生英文姓名 
WeiChung Ho 
電子信箱 
wise.jammy@gmail.com 
學號 
898620124 
學位類別 
博士 
語文別 
英文 
口試日期 
20170603 
論文頁數 
157頁 
口試委員 
指導教授廖述賢 指導教授謝佳琳 委員阮金祥 委員王中允 委員陳基國 委員林榮禾 委員張紘炬 委員徐煥智

中文關鍵字 
供應鏈網路設計
定址存貨
存貨路徑
遺傳演算法

英文關鍵字 
Supply chain network design (SCND)
locationinventory
inventoryrouting
genetic algorithms

學科別分類 

中文摘要 
供應鏈網路設計在供應鏈管理中占有很重要的地位，其目的在於提供供應鏈管理一個發揮效應的工作平台。供應鏈網路設計若依規劃時間的長短，可區分為戰術、戰略及操作等不同層級，其決策內容分別為設施定址，存貨及運輸規劃。以策略層級而言，因為投入的資源較大，決定後就不易改變，故一般多運用單期模式。以戰術層級而言，其目的係運用較近期的需求資訊，可依顧客的需求，劃分多期，求解最佳的運送及存貨規劃。由於供應鏈網路設計決策的時間軸不同，很難以同一模式建構完整的供應鏈網路，故本論文發展兩階層求解方法，第一階層為策略階段，求出最適的設施定址。第二階段為戰術階段，續依據策略階段的定址決策，發展最適的存貨與運送規劃。另基於策略階段決策，影響日後系統運作深遠，故本論文以不同的假設，設計三個不同的策略模式，因為本論文所探討的問題涵括了定址存貨、存貨路徑整合問題及路徑等問題，均屬困難性問題，囿於精確求解法只限求小、中型的問題，所以在本論文採用以遺傳演算法為基石求解上述所陳問題。 
英文摘要 
Supply chain network design (SCND) which provides an optimal configuration of platform is an important task in supply chain management. The SCND can be classified as strategic, tactical and operational level decisions depending on the time horizons, those decisions include the number, location of facilities, inventory policy and transportation plan. The strategic decision requires the vast investment; the decision is expected to operate for a long term. Therefore, it is reasonable to use singleperiod model. In the tactical and operational level, the demand information is more accurately, therefore, the decision maker needs a shortterm inventory and transportation planning which generate the product flows in the network. In this dissertation, we attempt to integrate a strategic and tactical plan and develop two stage models to construct the optimal SCND. In first stage, three strategic models are presented according to the various scenarios. In second stage, we propose a tactical model which is multi period inventory routing model for determining the time and amount of goods to deliver. Because these models consider locationinventory and inventoryrouting problem, both belong to NPhard problem, exact methods only can solve small or medium size problem, therefore a various genetic algorithms base heuristic are proposed the solve these problems. 
論文目次 
Index
List of Tables VI
List of Figures VII
Chapter 1.Introduction 1
1.1. Research background 1
1.1.1. Supply chain environment 1
1.1.2. Physically construction 3
1.1.3. DC implementation 4
1.2. Objective & Motivation 5
1.3. Research scope 6
1.3.1. Supply chain network configuration 6
1.3.2. Research methodology 7
1.4. Organization of the dissertation 7
Chapter 2.Literature Review 10
2.1. Location  Inventory models 11
2.2. Location  Routing models 14
2.3. Inventory  Routing models 17
2.4. Dual sale channel SCN 20
2.5. Multiple Criteria Decision Making (MCDM) 23
2.6. Research problem 26
Chapter 3 Design an integrated strategic and tactical SCND model 32
3.1. Introduction 32
3.2. The strategic stage 33
3.2.1. Inventory control decision 34
3.2.2. Transportation decision 36
3.3. The tactical stage 38
3.4. Solution methodologies 41
Chapter 4 Strategic planning  baseline model 45
4.1. Introduction 45
4.2. Problem statement 46
4.3. Model formulation and solution methodologies 49
4.3.1. Notation 49
4.3.2. Model formulation 50
4.3.3. Solution methodologies 53
4.3.3.1. Big clients allocation phase 55
4.3.3.2. Small clients allocation phase 56
4.3.3.3. Small clients routing phase 57
4.4. Experiment results 58
4.4.1. Evaluation instances 58
4.4.2. Computational results on the problem instances 60
4.5. Conclusion 64
Chapter 5. Strategic planning demand depend model 66
5.1. Introduction 66
5.2. Problem statement 66
5.3. Model formulation and solution methodologies 67
5.4. Numerical examples and sensitivity analysis 72
5.5. Conclusion 78
Chapter 6. Strategic planning multiobjective model 79
6.1. Introduction 79
6.2. Problem statement 82
6.3. Mathematical model and formulation 84
6.3.1. Proposed heuristic procedures 90
6.3.2. NSGAII for Pareto solutions 90
6.3.3. TOPSIS and Shannon entropy for the best compromise solution 92
6.4. Numerical experiment 94
6.4.1. Data Generation 94
6.4.2. Computational results 95
6.4.3. Sensitivity analysis 99
6.5. Conclusion 103
Chapter 7. Tactical planning DIRT model 105
7.1. Introduction 105
7.2. Problem statement 108
7.3. Model formulation and solution methodologies 111
7.3.1. Model formulation 111
7.3.2. Solution methodologies 113
7.3.2.1. Representation and initiation 114
7.3.2.2. Replenishment plan with capacity limitations 116
7.3.2.3. Lateral transshipment 116
7.3.2.4. Redistribution 117
7.3.2.5. Crossover operator 121
7.3.2.6. Mutation operator 123
7.4. Experiment 123
7.4.1. Experimental design 123
7.4.2. Computational results 124
7.5. Conclusion 130
Chapter 8.Conclusion and future work 131
8.1. Summary and research findings 131
8.2. Major contributions 132
8.3. Limitations of the research 134
8.4. Recommendations for future research 134
References 138
Appendix: Abbreviation table 156
List of Tables
Table 2.1 IRP variants structure 20
Table 2.2 A comparison for the proposed models and literature’s models 31
Table 3.1 The comparison between the syrategic and tactical planning 33
Table 3.2 The comparison for three strategic models 38
Table 3.3 Study key issues 40
Table 4.1 The model parameters for problem instances 59
Table 4.2 Computational results for P_50_300_T1_S2 60
Table 4.3 Computational results for nine problem instances 61
Table 5.1 Model parameters configuration for problem instances 73
Table 5.2 Parameters value for problem instances 74
Table 6.1 Nondominated solution set from NSGAII 97
Table 6.2 Computational results incurred from TOPSIS 98
Table 6.3 Computational results of 5 topranking solutions for P25_500 100
Table 6.4 Computational results of 5 topranking solutions for P25_800 101
Table 6.5 Computational results of 5 topranking solutions for P25_1000 102
Table 7.1 The cost components of the DIRPTR model 125
Table 7.2 The cost components of the IRPTOU model 126
Table 7.3 The cost components of the IRP model 127
Table 7.4 The stock level in the DIRPTR model for D10T10 instance 128
Table 7.5 The stock level in the IRPTOU model for D10T10 instance 128
Table 7.6 The stock level in the traditional IRP model for D10T10 instance 129
List of Figures
Figure 1.1 SCM activities 2
Figure 1.2 The VMI supply chain structure 3
Figure 1.3 The supply chain network 4
Figure 1.4 Supply chain model 6
Figure 1.5 The research framework 9
Figure 2.1 Classification of DRAI problems 19
Figure 3.1 SCM decision scheme 33
Figure 3.2 The process of the strategic models 34
Figure 3.3 Threelayer SCND order scheme 35
Figure 3.4 Twoechelon SCND distribution scheme 37
Figure 3.5 The process of tactical model 39
Figure 3.6 The tactical model distribution scheme 40
Figure 3.7 The strategic and tactical scheme 41
Figure 3.8 The general GA flow chart 44
Figure 4.1 The dual channel SCND structure 47
Figure 4.2 Flowchart of proposed heuristic method 55
Figure 4.3 The small clients cluster procedure 57
Figure 4.4 Small clients vehicle routing procedure 58
Figure 4.5 Tradeoff trends among costs in P_50_300_T1_S2 problem instance 61
Figure 4.6 Number of open DCs under various cost scenarios 62
Figure 4.7 The P_50_300_T1_S1 problem instance convergence trends 64
Figure 4.8 The diversity evolution for P_50_300_T1_S1 problem instance 64
Figure 5.1 The customers channel preference rate impact of number of open DC 76
Figure 5.2 The optimal open DCs number for various problem instance 76
Figure 5.3 The cost components for customers channel preference rate in case1 76
Figure 5.4 The cost components for customers channel preference rate in case2 77
Figure 5.5 The cost components for customers channel preference rate in case3 77
Figure 5.6 The cost components for customers channel preference rate in case4 77
Figure 5.7 The cost components for customers channel preference rate in case5 78
Figure 6.1 The flow chart of proposed heuristic procedure 89
Figure 6.2 The NSGAII solution scheme 91
Figure 6.3 The Paretofronts for the base line instances 99
Figure 6.4 The number of open DCs for various problem top ranking 103
Figure 7.1 The distribution scheme of the DIRPTR model 109
Figure 7.2 The system event of DIRPTR model 111
Figure 7.3 Twodimensional genetic solutions 115
Figure 7.4 The replenishment scheme with capacity limitation 116
Figure 7.5 The lateral transshipment algorithm 118
Figure 7.6 The modified replenishment and transshipment solution process 120
Figure 7.7 The vehicle scheduling 121
Figure 7.8 The crossover operator vehicle scheduling 122
Figure 7.9 The mutation operator vehicle scheduling 123
Figure 7.10 The models performance for the D10T10 instance 129
Figure 7.11 The DIRPTR cost component for the D10T10 instance 130

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