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研究生:高琳詠
研究生(外文):Lin-Yung Kao
論文名稱:應用人工智慧技術於自動檢驗儀器之效能最佳化
論文名稱(外文):Application of AI Techniques in Optimizing TLA Workflow
指導教授:陳永福陳永福引用關係
指導教授(外文):Yung-Fu Chen
學位類別:碩士
校院名稱:中國醫藥大學
系所名稱:醫務管理學研究所碩士班
學門:商業及管理學門
學類:醫管學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:64
中文關鍵詞:全自動檢驗系統排程人工智慧最佳化基因演算法
外文關鍵詞:Total Laboratory Automation (TLA)SchedulingArtificial Intelligence (AI)OptimizationGenetic Algorithm
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全實驗室自動化(Total Laboratory Automation; TLA)系統暨整合性實驗室網路已被證實可減少作業成本,改善臨床效能與經濟效益。署立台中醫院中央實驗室之全自動檢驗系統(TLA)系統建置後,有效地減少實驗室成本及人力。中央實驗室接收中部地區五家醫院之檢體,但檢體在進入TLA是隨機擺放,造成工作時會有某台檢驗機器因等待而閒置,增加檢驗時間,影響檢驗效率。本研究提出以人工智慧方法,尋求流程之最佳化,提升實驗室之工作效能。
本研究使用基因演算法,利用MTALAB設計最佳的工作排程,利用基因演算法在每個世代的演算中,保留較佳的個體來取代較不適合的個體以達成類似生物演化的過程,另外利用交叉重組及突變尋求全域最佳解,連續反覆進行最佳解之搜尋以獲得最短時間之排程。因為生化檢驗項目(機組三)比免疫檢查(機組一)及甲狀腺及B型肝炎(機組二)需要更長之檢驗時間,因此本研究著重於生化檢驗機器的工作排程設計,將工作平均分配於兩部機器,改善先前工作分配不平均之現象。
研究結果顯示利用基因演算法進行檢體的排序能夠有效減短16% TLA運作的時間,提升其檢驗效能。總而言之,本研究使用基演算法進行檢體排程,讓檢驗工作流程最佳化,有效降低檢驗時間與減少成本,進而提昇醫療品質。
Total laboratory automation (TLA) is the system which integrates laboratory instruments under a unified control with little or without human intervention. It has been demonstrated to be efficient in reducing operational costs and reducing working time, especially when integrated with consolidated network. Recently, a central laboratory equipped with TLA system was set up in central Taiwan area as a platform for performance evaluation. The preliminary study showed that the testing and processing time have been reduced for about 60% and the number of personnel has been decreased from 60 to 45 since its operation in March, 2006. However, it still needs to further enhance the workflow performance to meet the increasing number of samples when more and more hospitals are requesting the services of TLA.
Currently, the collected specimens from satellite hospitals were sorted randomly before entering the TLA system, which greatly decreases its working efficiency. The objective of this investigation is to design a genetic algorithm by using MATLAB toolbox to find the best solution for optimizing the TLA workflow, thereby increasing its efficiency. The experiment was done based on 5 batches of specimen. The result shows that application of genetic algorithm in arranging specimen sequences resulting in a decrease of 16% in average of TLA operation time. In conclusion, genetic algorithm is useful in increasing TLA efficiency by arranging the sequences of blood specimens before loading to the TLS system.
中文摘要 i
Abstract ii
致謝 iii
Tables of Contents iv
List of Tables v
List of Figures vi
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivations and Objectives 2
Chapter 2 Literature Review 5
2.1 Total laboratory automation system (TLA system) 5
2.2 Scheduling 6
2.2.1 Flowshop Scheduling Problem 6
2.3 Artificial Intelligent Techniques 7
2.3.1 Tabu Search 8
2.3.2 Genetic Algorithms 11
2.3.3 Characteristics of Genetic Algorithms 15
2.3.4 Ant algorithms 16
Chapter 3 Materials and Methods 20
3.1 Research Procedure 20
3.2 Setup of TLA system in CTNHU 20
3.3 The TLA Scheduling Problem 26
3.4 Operation Analysis 27
3.4.1 Logged File Analysis 30
3.5 The Proposed Model 32
3.5.1 Genetic Searching 33
3.5.2 Fitness Function 38
3.6 Experimental Procedure 38
3.6.1 Log File Processing 38
3.6.2 Experimental Procedure 40
3.6.3 Calculation of Turn-around Time 45
Chapter 4 Experimental Results 46
Chapter 5 Discussions and Conclusions 48
Bibliographies 50
Appendices 54
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