本研究的焦點主要是藉由不同變異程度上的需求與良率的不確定性生產規劃問題，來比較確定性和隨機性方法的表現。在不確定性的環境中，不確定的程度會影響管理者如何去產生未來不確定的系統值(system-values)，本研究有兩種建模的方法來表示需求與良率不確定的系統值。一般而言，不確定的參數可以假設為一個隨機變數。一個變異性較小的隨機變數，會以一個固定的期望值當作此隨機變數，供給第一種建模的方法來使用，以改善運算效率及簡單化數學模式，聲稱它為確定型方法。第二種方法稱隨機性模型，採用情境導向(scenario-based)的預測方法去產生數個配有機率的情境。所以本研究針對兩種未來不確定需求與良率的系統值，採用了兩種建模的方法。對於處理不確定性的生產規劃問題提出了四個方法，確定性良率確定性需求的方法(DDA)、確定性良率隨機性需求的方法(DDA)、隨機性良率確定性需求的方法(SDA)以及隨機性良率隨機性需求的方法(SSA)。本研究利用離散事件導向的滾動平面法模擬實驗來比較它們長期的效益。根據實驗的結果，DSA優於其他三種方法。

The focus of this study is to compare the performance of deterministic and stochastic approaches for production planning problem under various degrees of uncertainties in customer demand and production yield. In an uncertain environment, the degree of uncertainty affects how the manager to model uncertain future system values. In this study, there are two modeling methods to represent the uncertain system values of demands and yields. Normally, an uncertain future system value can be assumed as a random variable. For a random variable with small variance, to improve computation efficiency and simplify mathematical formulation, a constant value of the expected value for the random variable is used in the first modeling method, which is called deterministic approach. The second method, called stochastic approach, adopts scenario-based forecasting method that generates a number of scenarios with assigned probabilities. Since, in this study, there are two modeling methods for two types of uncertain system values, future demands and yields, totally four approaches are used to solve the uncertain production planning problem. They are deterministic-yield deterministic-demand approach (DDA), deterministic-yield stochastic-demand approach (DSA), stochastic-yield deterministic-demand approach (SDA), stochastic-yield stochastic-demand approach (SSA). This study compares their long-term effectiveness under rolling horizon practice by using discrete-event simulation experiments. According to the experiments, DSA outperforms the other approaches.

TABLE OF CONTENTS
摘要 I
Abstract II
LIST OF FIGURES VII
LIST OF TABLES IX
1. Introduction 1
2. Literature Review 7
2.1. Deterministic models 9
2.2. Stochastic models 10
2.3. The comparison of the two models 12
3. Approaches under Comparisons 14
3.1. Basic assumption 14
3.2. A deterministic-yield deterministic-demand approach (DDA) 15
3.3. A deterministic-yield stochastic-demand approach (DSA) 17
3.4. A stochastic-yield deterministic-demand approach (SDA) 18
3.5. A stochastic-yield stochastic-demand approach (SSA) 20
4. Experimental Design and Computational Results 22
4.1. Simulation for comparisons 22
4.2. Actual yield and actual demand 24
4.2.1. Actual yield generation 24
4.2.2. Actual demand generation 25
4.3. Forecast yield and forecast demand 27
4.3.1. Forecast yield generation 27
4.3.2. Forecasted demand generation 27
4.4. Parameters setting 30
4.5. Results and Analysis 32
4.5.1. Comparisons 32
4.5.2. Computation time 33
4.5.3. Performances under different control factors 34
5. Conclusion 53
Reference 54

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