應用基因演算法於醫療人員及診間之排程問題研究｜國立臺灣科技大學博碩士論文系統

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研究生：	呂佳靜 Chia-Ching Lu
論文名稱：	應用基因演算法於醫療人員及診間之排程問題研究 On the Study of Genetic Algorithm for Physician and Clinic Scheduling Problem
指導教授：	羅士哲 Shih-Che Lo
口試委員:	蔡鴻旭 Hung-Hsu Tsai 郭伯勳 Po-Hsun Kuo 羅士哲 Shih-Che Lo
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：	2018
畢業學年度：	106
語文別：	英文
論文頁數：	55
中文關鍵詞：	基因演算法、勞動力排程、醫療管理
外文關鍵詞：	Genetic Algorithms, Physician scheduling problems, Healthcare management
相關次數：	點閱：208 下載：4
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排程問題一直是工廠裡常見的問題之一，目的是縮短總製造時間並降低成本。近年來，勞動力排程問題也在各個領域裡逐漸受到重視，醫療人員的排班問題便是醫院裡重要的任務之一。
為了在不犧牲護理質量的情況下降低醫院的勞動成本，本文使用基因演算法(Genetic Algorithm, GA)來解決醫生輪值表的排程問題，我們同時考慮了醫生的需求和偏好以及醫院的資源，目標是使最少的病人看不到病，並且在醫院資源有限的情況下為醫生找到最佳的排班表。
我們設計了80個不同的問題來實驗，包含了醫生人數、診間數以及各項醫生需求及偏好皆不盡相同的情境，並使用具有基因演算法所有特徵的Evolver 4.0，也就是Microsoft Excel的GA Solver插件，滿足所有約束條件並且找到最佳解決方案。

Scheduling has always been one of the common problems in the factory, with the aim of reducing overall manufacturing time and reducing costs. In recent years, the problem of workforce scheduling has also gradually gained attention in various fields. The scheduling of medical personnel is one of the important tasks in hospitals.
In order to reduce labor costs in hospitals without sacrificing quality of care, this research uses the Genetic Algorithm (GA) to solve the physician rotation problems. We also consider the needs and preferences of doctors and the resources and costs of hospitals, the goal is to make the least patients who can not be examined in a visit and find the best schedule for physicians when the hospital resources are limited.
We design 80 different questions to experiment, including the different number of doctors and treatment rooms, and various needs and preferences of physicians, and we use the customized software Evolver 4.0, the GA Solver plug-in for Microsoft Excel to satisfy all constraints and find the best solution.

摘要    I
ABSTRACT    II
ACKNOWLEDGMENTS    III
FIGURES    VI
TABLES    VII
CHAPTER 1 INTRODUCTION    1
1.1 Motivation    1
1.2 Objectives    1
1.3 Research Structure    2
CHAPTER 2 LITERATURE REVIEW    4
2.1 Scheduling Problem Classify    4
2.2 Scheduling Solution    8
2.3 New Scheduling Problem    9
2.4 Genetic Algorithm (GA)    11
CHAPTER 3 PROBLEM FORMULATION AND GENETIC ALGORITHM    14
3.1 Problem Formulation    14
3.1.1 The Notation    14
3.1.2 Physician Scheduling Problem    15
3.2 Genetic Algorithm (GA)    17
3.2.1 Initialization    19
3.2.2 Fitness Function    19
3.2.3 Selection    20
3.2.4 Crossover Operators    20
3.2.5 Mutation Operators    21
3.2.6 Termination    22
CHAPTER 4 COMPUTATIONAL EXPERIMENTS    23
4.1 Experiment Environment    23
4.2 Research Assumption    23
4.3 Computational Results    24
4.4 Result Analysis    26
4.5 Summary    28
CHAPTER 5 CONCLUSIONS AND FUTURE RESEARCH    29
5.1 Conclusions    29
5.2 Further Research    29
REFERENCES    30
Appendix A. Example 9 in Case 1    33
Appendix B. Example 21 in Case 1    35
Appendix C. Example 37 in Case 1    37
Appendix D. Example 11 in Case 2    39
Appendix E. Example 26 in Case 2    41
Appendix F. Example 39 in Case 2    43

                                

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