需求及技術不確定性下之最佳技術投資組合與產能規劃模式

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研究生：	吳紹逸 Shaw-Yih Wu
論文名稱：	需求及技術不確定性下之最佳技術投資組合與產能規劃模式 An optimal technology portfolio adoption model considering capacity planning under demand and technology uncertainty
指導教授：	王孔政 Kung-Jeng Wang
口試委員:	曹譽鐘 Yu-Chung Tsao 游兆鵬 Chao-Pen Yu
學位類別：	碩士 Master
系所名稱：	管理學院 - 工業管理系 Department of Industrial Management
論文出版年：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	52
中文關鍵詞：	產能規劃、技術合併、馬可夫決策程序、差異化演算法、平行運算
外文關鍵詞：	capacity planning, technology portfolio, Markov decision process, differential evolution, parallel computing.
相關次數：	點閱：221 下載：2
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本研究提出一個在需求不確定性及技術創新下之資源投資組合規劃最佳化模型。此模型將同時提供決策者進行最佳技術選擇策略及產能與資源分配，期使利潤最大化。本研究利用馬可夫決策程序建立模型，每一行動(action)代表現階段投資的技術所需使用時間長度，亦代表一個子問題，每個子問題則以混和整數線性規劃建模為最佳產能與資源分配問題。本研究利用平行抽樣為基之差異化演算法進行每一個子問題的求解，而最佳遞歸函數則用於馬可夫決策模型的求解。為提高求解效率，本研究採用非可行解之修正機制於差異化演算法中。此外，本研究也使用平行運算，藉此減緩馬可夫決策模型高複雜計算之負擔。在實驗部分，本研究採用田口實驗方法進行敏感度分析，藉以找出演算法之最佳參數組合。本研究差異化演算法與其他被廣泛使用的演算法做比較，結果顯示其表現良好。最後，本研究亦評估在不同需求變異水準下對預期利潤所造成的影響。

One of the most challenging issues for industry is how to tackle technology adoption and capacity planning simultaneously under uncertainty. In this research, a technology portfolio adoption model considering capacity planning under demand and technological uncertainty is proposed. The model optimizes technology portfolio and simultaneously addresses the optimal capacity planning to maximize the profit in a planning horizon. The problem is modeled by Markov decision process (MDP), of which each action is presented as a desired length of time to retain the currently used technologies, in which the capacity planning problem is modeled by a stochastic mixed linear integer programming (SMLIP) problem. For achieving an efficient solution approach, a parallel sampling-based differential evolution (PSDE) algorithm is employed to solve the SMLIP problem. After that, an optimality backward recursive function is proposed to solve the MDP problem. Further, a parallel computing technique is utilized to relax the computational burden of the MDP model. In the experiment, a sensitive analysis is conducted to investigate effects of the algorithm parameters by using Taguchi method. Furthermore, a performance comparison among PSDE and other popular algorithms is conducted. Finally, we evaluate the impact of different levels of demand variance and risk of investment on the expected profit.

Contents
致謝.......................................................................I
Abstract.................................................................III
摘要......................................................................IV
List of Table............................................................VII
List of Figure...........................................................VII
Chapter 1 Introduction.....................................................1
1.1	Research background and objective..................................1
1.2	Research motivation................................................2
1.3	Research organization..............................................3
Chapter 2 Literature review................................................4
2.1	Capacity planning considering technology uncertainty...............4
2.2	Capacity planning considering demand uncertainty...................5
2.3	Differential evolution algorithm (DE)..............................6
2.4	Summarizing........................................................7
Chapter 3 Model formulation................................................8
3.1	Modeling on technology portfolio...................................8
3.2	Modeling on Capacity planning.....................................12
Chapter 4 Solution procedure..............................................16
4.1	Sequential DE algorithm with solution repairing mechanism.........16
4.1.1	Initial population................................................16
4.1.2	Mutation..........................................................17
4.1.3	Crossover.........................................................17
4.1.4	Selection.........................................................18
4.2	The proposed parallel sampling-based differential evolution algorithm.................................................................20
Chapter 5 Experiment result...............................................23
5.1	Optimal parameter setting.........................................23
5.2	Algorithm benchmarking............................................26
5.3	Suggested capacity planning and technology portfolio adoption.....30
Chapter 6 Conclusion......................................................34
Reference.................................................................35
Appendix..................................................................38

                                

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