臺灣博碩士論文加值系統

雲端運算的興起，不僅帶動了網路服務的應用，也讓企業資源規劃系統發展另一種使用面向。透過網路服務的設計標準，將來企業對於企業資源規劃系統的選購不再視為一個套裝軟體，而是可由不同功能模組和不同供應商組合成企業資源規劃系統，並且結合雲端運算的計算能力和託管服務，帶給企業易於使用與成本降低兩個優勢。因此，本研究提出一個企業資源規劃系統的新架構，企業可在雲端平台上量身訂製企業資源規劃系統，我們稱為CloudERP。但如何幫助企業組合CloudERP系統將是一大問題，故提出基因演算法結合約略集合理論發展一套評選CloudERP組合的方法，幫助企業找出最適合的CloudERP組合。最後在Google App Engine上發展雛型系統，以實驗的方式來證明基因演算法與約略集合理論應用於CloudERP組合評選上的成效。

Cloud computing not only urges the application of Web services, but also develops the other view of ERP. Through the design standard of Web services, the enterprise won`t see ERP as a package software, but a system which is combined with different function models and suppliers, and contains the computing ability and manage service, so that the enterprise would get the advantages: easy to use and reduce the cost. Therefore, this research creates a new idea of ERP that the enterprise can order ERP on Cloud platform called CloudERP. But how to help the enterprise composite the CloudERP is a huge problem, we propose a method of selecting CloudERP combined with rough set theory and genetic algorithm to help the enterprise find the best suited one. A genetic algorithm has been widely used to solve optimization problems for large scale and complex systems. However, when insufficient knowledge is incorporated, genetic algorithm is less efficient in terms of searching for an optimal solution. This paper develops a systemic but complete composition process which a generic genetic algorithm incorporating knowledge extracted from the rough set theory. Finally we develop the prototype of the CloudERP selection on Google App Engine platform, and prove the effect how genetic algorithm and rough set theory are applied in CloudERP by experiment.

中文摘要 i
Abstract ii
誌 謝 iii
目 錄 iv
圖索引.. v
表索引.. vi
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 4
1.3研究架構與流程 5
1.4研究限制與範圍 6
第二章 文獻探討 7
2.1 Web-based ERP 7
2.2網路服務 9
2.3評選企業資源規劃系統 12
第三章 研究方法 15
3.1 CloudERP基本描述 15
3.2 約略集合理論 18
3.3 基因演算法 37
3.4 CloudERP評選方法 46
3.4.1結合基因演算法與規則的CloudERP組合評選流程 46
3.4.2規則的屬性採用 53
3.4.3約略集合的預先處理 57
第四章雛型系統與實驗分析 61
4.1系統描述與建構 61
4.2範例說明 64
4.3實驗研究 70
4.4實驗分析與討論 76
第五章 結論 79
參考文獻 80

圖索引
圖1-1 研究流程圖 6
圖3 1 CloudERP運作流程圖 16
圖3 2 上下界近似 22
圖3 3 基因演算法流程圖 38
圖3-4 常見的編碼方式 39
圖3-5 單點交配(灰框為交配部份) 41
圖3-6 兩點交配(灰框為交配部份) 42
圖3-7 均勻交配(灰框為交配部份) 42
圖3-8 單點突變(灰框為突變部份) 43
圖3 9 限制式基因演算法流程圖 53
圖4-1 CloudERP使用者選購畫面 62
圖4-2 最佳CloudERP組合評選結果 63
圖4-3 CloudERP產品元件部分原始資料 63
圖4-4 限制性基因演算法收斂圖 69
圖4-5 限制性基因演算法之敏感度分析 74

表索引
表2-1網路服務組合相關研究 11
表3 1 原始資料表 20
表3-2不可區分關係 21
表3-3 移除的屬性對不可需分關係子集合的影響 24
表3-4 識別矩陣 25
表3-5 經識別矩陣處理後的資料 26
表3-6 識別矩陣 27
表3-7 經識別矩陣簡化後的資料 27
表3-8 決策表 29
表3-9 基本集合 30
表3-10 上下界近似與準確率 30
表3-11 識別矩陣 31
表3-12 折減後的決策表 31
表3-13 識別矩陣 32
表3-14 最後的決策表 32
表3-15 約略集合理論之相關研究─決策規則 35
表3-16 約略集合裡混之相關研究─分類 36
表3-17 基因演算法之相關研究 45
表3-18 屬性的描述 57
表3-19 決策表 58
表3-20 基本集合 58
表3-21 上下界近似與準確率 59
表3-22 屬性的折減 59
表3-23 最終的決策表 59
表3-24 規則集合 60
表3-25 決策規則的說明 60
表4-1 初始族群 66
表4-2 第15代母體 67
表4-3 第40代母體 表4-4 第70代母體 68
表4-5 範例最佳CloudERP組合之屬性資料表 69
表4-6 小樣本(50個)項目之各演算法比較表 72
表4-7大樣本(100個)項目之各演算法比較表 73
表4-8不同母體大小的命中率 75
表4-9不同交配率的命中率 75
表4-10不同突變率的命中率 75

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