研究生: |
胡志翰 |
---|---|
論文名稱: |
整合資料挖礦與遺傳演算法之混合式排程求解方法及實證 A study of the integrated method of data mining and Genetic Algorithms apply to hybrid scheduling problem with An Empirical Case |
指導教授: | 簡禎富 |
口試委員: | |
學位類別: |
碩士 Master |
系所名稱: |
工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management |
論文出版年: | 2004 |
畢業學年度: | 92 |
語文別: | 中文 |
論文頁數: | 53 |
中文關鍵詞: | 醫療服務 、復健 、排程 、混合式排程 、屬性歸納 、基因演算法 |
外文關鍵詞: | Hospital Management, Rehabilitation, Scheduling, Hybrid Shop, Attribute-oriented induction, Genetic Algorithms |
相關次數: | 點閱:3 下載:0 |
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近年來醫療業對於服務的品質越來越加重視,醫院不僅需具備優越的醫療技術及設備,而能否提供更貼心的醫療服務,往往是病患選擇求診醫院的關鍵。因此,醫院無不竭盡所能的提升醫療服務品質,應用工業工程與科學管理的方法妥善的規劃醫療資源,進而加強對病患的醫療服務。除了卓越的醫療技術外,完善的醫療服務,將成為醫院的競爭力核心,也是目前醫院經營的重要課題。
本研究目的為應用科學管理的方法於醫療服務的範疇,針對物理復健排程問題,建構學習法則訓練模式進行求解。物理復健排程問題屬於混合式的排程問題,研究中將應用屬性歸納的方法去擷取基因演算法的資料樣型,從中進行樣型的分析,建構學習法則的歸納模式,最後推導出學習法則,本研究希望藉由學習法則的便於應用、快速求解的優點,應用於物理復健排程問題的求解,將更有效率的進行排程規劃。最後本研究應用新竹某一教學醫院復健部門之歷史資料進行實證,應用屬性歸納方法推導出學習法則,並進行復健排程的規劃。經由實驗結果可知本研究可在較短的時間且節省運算成本的情況下,得到可行的排程規劃,在此適當的求解品質下,可改善物理復健部門之資源規劃效率。
Besides excellent medical techniques, perfect service has became the core competence. The hospitals emphasize service quality than ever recently. Providing considerable medical service is more important than advanced medical equipments. Hospitals do the best to improve service quality that is always the key factor to choice the hospital. The engineers can also use scientific methods to find proper resource distribution and enhance the service quality.
This research use scientific methods to construct the learning rules for physical therapy scheduling problem. This study use attribute oriented induction methods to extract the pattern based on GA methods and construct the potential rules in therapy scheduling problem. This method provides the advantages of efficiency and near optimal solution on GA. Finally, we have a numerical illustration in physical therapy scheduling problem based on results from proposed learning rules. And the experimental results show that proposed methods can improve the efficiency and cost to find the near optimal solution in such scheduling problem.
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