本研究在需求不確定的環境下，將考慮風險趨避之二階隨機多廠區產能投資規劃問題-以TFT-LCD產業為例。以往研究是以利潤最大化或成本最小化做為產能投資規劃的目標，而忽略實行決策時的風險，故本研究採用Conditional Value-at-Risk(CVaR)值作為風險評估指標，求得穩健(robust)產能擴充與產能分配決策的有效集合(Capacity Portfolio Efficient Set)，期望同時達到最大化總利潤與最小化風險兩大目標。由於隨機多目標產能規劃模型具有多目標與隨機性兩種特質，故提出以連續抽樣法(Sequential Sampling)，將模型由隨機多目標規劃問題轉換成確定型單目標規劃問題之大型混整數規劃模型(Mixed integer linear programming, MILP)，再利用混合整數規劃求解軟體CPLEX及本研究所提出的Expected shadow price decomposition (ESPD)演算法，求得有效產能投資曲線與集合。最後以TFT-LCD產業案例資料驗證本研究所提出的產能規劃模型與演算法架構之有效性與可用性，並進行演算法效率分析以針對各參數進行敏感度分析。然而，當問題複雜度提升時，混合整數規劃求解軟體將面臨因問題複雜度過高以至於無法求得最佳解，反之線性規劃之演算法仍能求得最佳解。故本研究針對多目標隨機產能規劃問題進行求解，以獲得最佳產能規劃決策。

This research focuses on the capacity planning problem at uncertain environment. Risk-averse two-stage stochastic programming for multi-site capacity planning in TFT-LCD manufacturing is considered. Maximizing the profit or minimizing the cost is the goal of capacity investing planning problem in traditional research. However, this measure ignores the risk of making decisions. Therefore, this research uses Conditional Value-at-Risk (CVaR) as the index of risk evaluating. Because the random multi-goals capacity planning problem has properties of multiple goals and randomness, sequential sampling is applied for transforming the original problem into mixed integer linear programming (MILP). Next, adopt CPLEX and expected shadow price decomposition (ESPD) proposed in this study to acquire the effective capacity investmenting curve. Last, verifying the effectiveness and feasibility of the capacity planning model proposed in this study through applying on TFT-LCD industry real case data. Sensitivity analysis for each parameter and the efficiency evaluation of the algorithm are also mentioned. The result shows that CPLEX result when the complexity of the problem is too high. On the contrary, linear programming-like algorithm is able to get optimal solution under this situation.

摘要 I
Abstract II
誌謝 III
Content IV
List of Tables VI
Chapter 1 Introduction 1
Chapter 2 Literature review 4
2.1 Stochastic applications 4
2.2 Risk measure in stochastic planning 5
2.2.1 Risk-neutral decision making 6
2.2.2 Risk-neutral problems 7
2.3 Risk measures in stochastic programming problem 8
2.3.1 Variance 8
2.3.2 Value-at-risk 8
2.3.3 Conditional Value-at-Risk 9
2.4 Related literature in risk measure 10
2.4.1 Supply chain management problem 10
2.4.2 Resource allocation problem 12
Chapter 3 Methodology 14
3.1 Mean-CVaR stochastic programming for profit- optimization problem 14
3.2 Risk-averse stochastic multi-resources capacity investment 18
Chapter 4 Solution algorithms 27
4.1 Expected shadow-price based algorithms 27
4.2 Capacity allocation phase 28
4.3 Capacity expansion phase 29
4.4 Algorithm 31
Chapter 5 Computational experiments 35
5.1 Case study 35
5.2 Comparative results between profit and risk measure 40
5.3 Comparative results between deterministic model and stochastic models 50
5.4 Comparative results between different demands for stochastic model 59
5.5 Comparative results between expected profit and risk measure 64
Chapter 6 Conclusions and future study 66
References 67
Appendix: Data of the case study 71

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