臺灣博碩士論文加值系統

風險中立的概念普遍用於存貨模型中，其補貨策略在於求取規劃週期中之期望利潤最大化或預期成本最小化。本研究將風險趨避之概念加入存貨模型中，考量決策者在風險趨避之情況下，如何在多期補貨模式中決定存貨的最佳水準。
本研究模型建構的方法在於最大化利潤現金流之預期效用。同時採用定期訂購的存貨補貨機制，假設模型只允許缺貨後補的情況。並且討論需求風險隨規劃週期增加而變大時，最佳存貨水準如何變化。分析成本、後補係數，以及其他模型參數如何影響最佳存貨水準。研究發現越是風險趨避之決策者在決定其最佳存貨水準會比較不趨避之決策者來得低。

Risk-neutral is a well-known concept in inventory models. It is characterizing replenishment policies that maximize expected total profit being equal to minimize expected total cost over the planning horizon. In this study, we incorporated the risk-averse concept into our model by considering a risk-averse decision maker who determines optimal base stock level in multi-period planning horizon.
Our approach of modeling risk aversion is to maximize the expected utility of the present value of net profits. We adopt the periodic order-up-to S inventory replenishment policy and consider back-order case only. We discuss the variance of the demand is increased by period t and how to effect the change of the base stock level in different utility functions. And we carry out the sensitivity analysis and it is analyzed how costs and back-order factor, others model parameters as well as risk-free interest rate influence the base stock level. In addition, we conclude that a risk-averse decision maker with a more concave utility function sets his base stock level to a lower value than the less risk-averse one.

CHINESE ABSTRACT I
ENGLISH ABSTRACT II
CONTENT III
LIST OF FIGURE IV
LIST OF TABLE V
CHAPTER 1 INTRODUCTION 1
1.1 BACKGROUND AND MOTIVATION 1
1.2 RESEARCH OBJECTIVE 3
1.3 THESIS FRAMEWORK 4
CHAPTER 2 LITERATURE REVIEW 6
2.1 EXPECTED UTILITY APPROACH 7
2.2 CAPITAL ASSET PRICING MODEL 11
2.3 THE MAIN DIFFERENCE IN OUR RESEARCH 14
CHAPTER 3 THE MODEL 16
3.1 PROBLEM DESCRIPTION 16
3.2 MODELING 18
3.2.1 The infinite horizon problem 18
3.2.1.1 Single period 18
3.2.1.2 Multi-period 19
3.2.1.3 Solution to the risk aversion 22
3.2.1.4 Numerical results 23
3.2.2 The proof of comparing risk-averse and risk-neutral 27
3.2.3 The finite horizon problem 28
3.2.3.1 Single period 28
3.2.3.2 Multi-period 29
3.2.3.3 Numerical results 30
CHAPTER 4 SENSITIVITY ANALYSIS 33
CHAPTER 5 CONCLUSIONS 40
REFERENCES 41
APPENDIX 44

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