臺灣博碩士論文加值系統

企業在進行決策規劃時，會先由高階決策者訂定整體的方向和目標，再將任務細分給次階的決策人員施行並達成次目標，這種階層式的決策問題可以經由多階的數學規劃來進行模擬。其中高階決策者和次階決策者各自控自不同的變因，卻相互受到牽制，而高階決策者對變因的控制也可視為對次階目標的一種試探性行為。
由於粒子群最佳化演算法具有模仿生物群體依賴相似特性之群體智慧(Swarm Intelligence)的概念方法，及粒子經驗交換及傳承世代之演算模式，其利用粒子族群具有探測(Exploitation)與開發(Exploration)的特色，可用於問題空間中搜尋全域的最佳解。本研究將運用粒子群最佳化演算快速收斂的特性來找出最低成本解，並發展出適用於求解線性和線性二階規劃的改良式粒子群最佳化，並將演算結果和過去使用基因演算法進行討論分析。結果顯示傳統粒子群和本研究所改良的粒子群演算法，再求解線性二階規劃的能力上皆有優異的表現，而其中又以改良式粒子群演算法的求解結果較為突出。

In decisions making for an organization, the upper-level decision maker has to determine the operation direction and goals first, and then forward them to the subordinate level as the base of decision making. Basically, subordinate-level manager has to achieve his/her goal without conflicting to high-level decision. This kind of hierarchical characteristics can be modeled and programmed by using mathematical programming.
Particle Swarm Optimization (PSO) can mimic cooperation between individuals in the same group by using swarm intelligence and exchange experiences from generation to generation. To exploit and explore the hyperspace global optimal with PSO has many advantages, especially converges fast. This research attempts to develop a noval PSO named vector-controlled particle swarm optimization (VCPSO) to solve bi-level programming more accurately with comparison to genetic algorithm (GA) . The experimental results show that the proposed VCPSO is able to converge faster and has better accuracy than conventional PSO and GA.

摘　要 I
ABSTRACT II
誌 謝 III
目錄 V
表目錄 VIII
圖目錄 X
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍 2
1.4 論文架構 2
第二章 文獻探討 4
2.1史達勃克賽局模式 4
2.2多階規劃 Multi-level programming: 5
2.2.1 線性二階規劃問題之定義 7
2.2.2 線性二階規劃之幾何特性 9
2.2.3 線性二階規劃解之特性 9
2.2.4 線性二階規劃之求解方法 10
2.2.5 其它相關之問題模式 14
2.2.5.1 三階規劃(three-level programming) 14
2.2.5.2 二階混合整數線性規劃 15
2.2.5.3 多個低階決策單位的線性二階規劃 16
2.3 粒子群最佳化(Particle Swarm Optimization) 18
2.3.1 粒子群最佳化之原理與發展 18
2.3.2 粒子群最佳化演算法 20
2.3.3 粒子群最佳化之應用 22
第三章 研究方法 25
3.1 研究架構 25
3.2 粒子群最佳化應用於線性規劃 27
3.2.1 粒子群最佳化求解線性規劃 27
3.2.2 改良粒子群最佳化求解線性規劃 29
3.3 粒子群最佳化應用於線性二階規劃 34
3.3.1 粒子群最佳化求解二階線規劃 34
3.3.2改良粒子群最佳化求解線性二階規劃 36
3.4 小結 38
第四章 實驗分析與驗證 39
4.1 實驗例子簡介 39
4.2 演算法評估 40
4.3粒子群最佳化求解線性規劃數值驗證 41
4.3.1 粒子群最佳化與模糊類神經網路求解比較 41
4.3.2 粒子群最佳化求解線性規劃的參數敏感度分析 44
4.4粒子群最佳化求解線性二階規劃數值驗證 52
4.4.1 粒子群最佳化與其他方法之比較 52
4.4.2粒子群最佳化求解線性二階規劃的參數敏感度分析 59
4.5 資料驗證小結 69
第五章 結論與建議 70
5.1 結論 70
5.2 研究貢獻 72
5.3 未來研究建議 72
參考文獻 73
附錄A 實驗結果 81

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