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系統識別號 U0002-2706201722164500
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 Wei-Chung 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 2017-06-03
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)  location-inventory  inventory-routing  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 single-period model. In the tactical and operational level, the demand information is more accurately, therefore, the decision maker needs a short-term 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 location-inventory and inventory-routing problem, both belong to NP-hard 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 multi-objective 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. NSGA-II 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 Non-dominated solution set from NSGA-II 97
Table 6.2 Computational results incurred from TOPSIS 98
Table 6.3 Computational results of 5 top-ranking solutions for P25_500 100
Table 6.4 Computational results of 5 top-ranking solutions for P25_800 101
Table 6.5 Computational results of 5 top-ranking solutions for P25_1000 102
Table 7.1 The cost components of the DIRPTR model 125
Table 7.2 The cost components of the IRPT-OU 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 IRPT-OU 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 Three-layer SCND order scheme 35
Figure 3.4 Two-echelon 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 Trade-off 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 NSGA-II solution scheme 91
Figure 6.3 The Pareto-fronts 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 Two-dimensional 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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