本研究主要探討大型量販店物流網路之區位定址問題，並加入供應商管理庫存策略，並整合了供應鏈供應商選擇、庫存控制與運輸等供應鏈中重要議題，以兩階層的供應鏈為例，由供應商將商品成品運送至配銷中心，配銷中心再將商品成品送至消費者手中。
由於本模式所考量的目標為總成本最小、服務回應率最大、商品訂單達交率等三大目標，並非單一的目標，所以我們使用了基因遺傳演算法(NSGA-II)求解混合非線性整數規劃問題，並採行兩個貪婪法則來使用基因遺傳演算法求取柏拉圖最佳解。
依據所建立問題之混合式基因演算法所寫的MATLAB程式進行相關的進行模式求解與數據分析，以便能瞭解數學規劃模式中相關參數與不同因子的變化，對於整合性物流網路選址問題當中之最適解所產生的影響性。
最後，我們設計了不同情境，將不同目標函數賦予不同的權重值，以得知對於不同目標函數權重的情境明顯會影響配銷中心的地點設置之決策，再由決策者依據所得的結果選擇適合的方案，最後再把成本項細分為六項，探討各項成本的影響程度。

The study focused on the Facility Location Problem (FLP) problem in a logistics network for a large retail store in Taiwan. We also add in the VMI strategy and also provide the solutions for the supplier selection, inventory control, and transportation decisions. Using a two-stage supply chain with as an example, the suppliers send product to distribution centers (DCs) and DCs send products to consumers.Because the goal of our three objective are to make the total cost smallest, Responsiveness Level biggest and volume fill rate biggest, not single objective, so we use NSGA-II to solve mixed nonlinear integer programming problem and take two greedy approach to use heuristic genetic algorithm and find the optimal solution of Pareto. According to the established model and we used the software of MATLAB to solve and analyzed the data, then we can understand the change of our model’s parameters and different factors, than we can realize the effects of the optimal Pareto solutions.
Finally, we designed several different conditions and distributed different weight to different objective, so we can know different condition would obviously affect the strategy of facility location, than the decision maker could accord to the result and found a solution. After we got the result, we divided the total cost into six items of cost, and tried to know the impact of the six different items.

目錄
頁次
目錄 I
圖目錄 III
表目錄 V
第一章 緒論 1 
1.1 研究背景與動機 1 
1.2 研究目的 5
1.3 研究範圍與限制 6
1.4 研究流程與架構大綱 8
第二章 文獻探討 10
2.1 設施區位定址問題 10
2.1.1 設施區位定址之基本概念 10
2.1.2 設施區位定址問題與銷售物流網路設計 13
2.2 供應商管理庫存(Vender Managed Inventory) 15
2.2.1 庫存政策 15
2.2.2 供應商管理庫存定義與基本概念 17
2.2.3 供應商管理庫存VMI的效益 20
2.2.4 供應鏈管理庫存VMI之阻礙 21
2.3 整合性供應鏈模式 23
2.4 多目標規劃法(Multiobjective Evolutionary Algorithm) 29
2.4.1 多目標規劃法之基本概念 29
2.4.2 柏拉圖最佳化基本概念 30
2.4.3 傳統多目標規劃法 32
2.5 多目標遺傳演算法 35
2.5.1 第一代多目標遺傳演算法 36
2.5.2第二代多目標遺傳演算法 37
第三章 模式建構 39
3.1 模型之基本架構想法 39
3.2 研究問題與假設 43
3.3 模型之建構與求解 46
3.3.1 模型符號說明 46
3.3.2 模型之目標函數 48
3.3.3 整合庫存控制與銷售物流網路之多目標設址模式 54
3.4 小結 56
第四章 問題求解方法 57
4.1 多目標基因遺傳演算法 57
4.2 第二代非支配基因演算法(NSGA-II) 58
4.3 求解方法 61
第五章 案例設計與分析 66
5.1 案例設計說明 66
5.2 案例求解 71
5.3 案例分析 72
第六章 結論與建議 83
6.1 研究結論 83
6.2 管理意涵 84
6.3 未來研究方向與建議 86
參考文獻 88
附錄 97

圖目錄
頁次
圖1-1 物流基本架構 2
圖1-2 本研究流程架構 8
圖2-1 銷售設計網路中的四個戰略計劃 14
圖2-2 基本商業模式與VMI商業模式之比較 18
圖2-3 VMI概念架構圖 19
圖2-4 整合性供應鏈模式 23
圖2-5 柏拉圖最適解集合 31
圖2-6 傳統多目標規劃法 33
圖3-1 設置於採購地區之物流中心架構 40
圖3-2設置於賣場地區之物流中心架構 40
圖3-3設置於兩端之物流中心架構 41
圖3-4 設立於同一區域之物流中心架構 42
圖3-5 本研究之分銷網路系統架構圖 43
圖3-6數學模型之概念圖形 48
圖3-7 集合覆蓋問題 52
圖4-1 典型的基因遺傳演算法架構 57
圖4-2 非支配解排序過程 59
圖4-3 NSGA II的演算流程 59
圖4-4 混合式基因遺傳演算流程圖 61
圖4-5 基因編碼之後的染色體 64
圖5-1 案例之物流架構 66
圖5-2演算法第一代與第三次演算結果 71
圖5-3 演算法前1/4與最後一代演算結果 71
圖5-4 情境一(以成本為主)最佳解選址結果 78
圖5-5 情境二、三(以消費者滿意度為主)最佳解選址結果 78
圖5-6情境四(三個目標偏好程度一致)最佳解選址結果 79
表目錄
頁次
表2-1 VMI效益之文獻 20
表2-2 多目標確定性模式研究文獻 24
表2-3 傳統多目標最佳化方法 33
表5-1 案例之供應商 67
表5-2 案例之配銷中心(DC) 67
表5-3 案例之消費者來源 67
表5-4柏拉圖前緣解與決策結果74
表5-5 第6、19、27組解對應之目標函數值	- 76 -
表5-6 不同情境之下之柏拉圖最佳解與對應參數	- 77 -
表5-7 柏拉圖前緣解之成本項目百分比	- 79 -

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B.中文部分－
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