臺灣博碩士論文加值系統

在全球化競爭的情況下，商業交易環境已從以往企業與企業間的競爭演變到不同供應鏈體系間的競爭。如今供應鏈管理(Supply Chain Management，SCM)成為各企業在經營管理上的一個重要課題，主要是將企業間各功能部門間相關的運作程序加以整合協調之合作策略，其涵蓋範圍可從物料取得，經過設計、生產、庫存管理、行銷與售後服務等。供應鏈在變動環境中，存在著許多不確定性，其中需求不確定性即是由客戶的需求變異所產生，為許多企業所欲掌握的一個重要部分，因此需求預測為一重要因素。近年來，許多研究開始應用模式預測控制(Model Predictive Control，MPC)在存貨控制或供應鏈管理上，與傳統最佳化成本隨機規劃法(cost-optimal stochastic programming solutions)相比，所需之技巧較為簡易，也不須建構在許多假設下；在資訊分享、路網架構以及限制處理上皆較具彈性。因此本研究欲發展一模式預測控制模式，模式預測控制為一控制理論，包含決策變數的更新以及需求預測模式，以能在相關因素變動時尋找最佳決策變數，進行決策。
本研究主要構建動態供應鏈管理模式，並將模式預測控制應用於供應鏈管理，首先定義供應鏈組成架構，並分析架構階層間之關係及限制，依此構建供應鏈模式。接著構建模式預測控制，包含決策變數及需求預測，由於過去及現在的控制行為會影響未來系統的回應結果，而滾動平面法(rolling horizon approach)可藉由結合過去及現在的控制行為預測未來之控制行為，因此選用此一方法進行控制行為，在系統因素或環境有所變動時進行決策更新。此外，構建一自我迴歸移動平均整合(Auto Regressive Integrated Moving Average，ARIMA)時間序列模型進行需求預測。
實驗案例設計上分為兩部份，首先建立需求預測模型，再進行最佳化求解。在需求預測模型之建立上，其實驗數據來源以實際訂單數列資料校估獲得預測模型參數。供應鏈所需資料則由不同型態之虛擬資料進行最佳化以獲得決策，以驗證模型。期望能完成基礎之模式預測控制模型，以能在不同產業下彈性修正模型，提供產業決策建議。

In the global competition, Supply Chain Management (SCM) plays an important role in enterprise operations and management. SCM is developed to coordinate organizational operations, which encompassing raw materials, manufacturing, sales and service to serve customers. There are lots of uncertainties exist in the Supply chain due to the violent market environment Among these uncertainties, demand uncertainty is originated from the customer''s demand. Therefore, demand forecasting is an important factor in SCM. Recently, lots of researches focus on applying Model Predictive Control (MPC) in inventory control or SCM. The MPC approach provides the flexibility in information share, network structure, and constraint processing. This research proposes a MPC model to model the demand forecasting problems with updating process for decision variables.

(the supply chain structure) This research constructs a dynamic SCM model, and applies MPC to the supply chain management. The dynamic SCM model is constructed based on a proposed supply chain structure, and elements in the SCM are analyzed to explore the relationship and restriction of variables among the echelon structure. In demand forecasting, the Auto Regressive Integrated Moving Average (ARIMA) model is implemented to forecast future demands based on historical sale records. The MPC includes decision variable and demand forecasting models. Based on the past and present control actions, a rolling horizon approach is proposed to predict future control strategies. In addition, construct for demand forecasting.

In order to illustrate the MPC model, two case stuies are conducted, namely, demand forecasting model and optimization model. The demand forecasting model is calibrated based on the actual data. The proposed supply chain system is verified by numerical analysis through a set of simulated data.

誌謝 I
中文摘要 II
英文摘要 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章 緒論
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究方法與流程 3
第二章 文獻回顧
2.1 供應鏈 5
2.2 供應鏈問題描述 7
2.3 供應鏈管理的求解方法 8
2.4 供應鏈管理之不確定性因素 11
2.5 時間序列型的預測方法 14
2.5.1 移動平均法 15
2.5.2 指數平滑法 16
2.5.3 迴歸分析法 16
2.5.4 自我迴歸移動平均整合法 16
2.6 模式預測控制 20
2.7 滾動平面法 23
第三章 供應鏈模型架構之建立
3.1 供應鏈模式 25
3.1.1 定義供應鏈問題 26
3.1.2 模式構建 28
3.2 模式預測控制架構 33
3.3 需求預測模式 35
3.4 最佳化模式 36
第四章 模式求解演算法之建立與探討
4.1 整體模式求解架構 39
4.2 需求預測 40
4.3 最佳化求解 46
第五章 數值實驗
5.1 實驗設計 48
5.1.1 背景介紹 48
5.1.2 實驗設計 50
5.1.3 實驗資料 53
5.2 多階層單一產品實驗 56
5.2.1 需求預測模型 56
5.2.2 最佳化 60
5.2.3 結果討論 72
5.3 多階層多產品實驗 73
5.3.1 需求預測模型 73
5.3.2 最佳化 80
5.3.3 結果討論 94
第六章 結論與建議
6.1 結論 96
6.2 建議 97
參考文獻 98
附錄
附錄一 自行車出口數量及出口單價資料 101
附錄二 多階層多產品最佳化結果 106
附錄三 應用CPLEX求解模式程式 115

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