根據台灣衛生福利部1982年至2016年發布的死亡報告，台灣國人最高死因在此年間皆為癌症。而強化調製放射性治療（IMRT）是癌症患者治療中最重要的治療方法之一。癌症患者治療的等待時間對患者的生存率有負面影響，因此降低病患等待接受放射性治療此問題的重要性。然而台灣的放射性治療中心調度分佈主要仍以人工處理，缺乏一套完整的病患排程機制。
本研究在考慮機器容量的限制、患者類型、機器類型以及患者最長可等待時間等條件後，提出新的數學模型來進行放射性治療病患排程。此外本研究以實際放射性治療排程資料搭配實驗設計分析驗證基因演算法之績效。本研究將理論研究與實際應用相結合，開發出適用於放射性治療患者排程問題的啟發式基因算法。可以提高效率、減少等待時間，進而提高設備的有效利用率、提高放療患者的舒適度並降低癌症死亡率。

According to Taiwan's Ministry of Health and Welfare's release of the death report from 1982 to 2016, all causes of death in Taiwan have the highest cancer death rates. Intensive-Modulated Radiation Therapy (IMRT) is one of the most important treatments in the treatment of cancer patients. It has also been pointed out that the waiting time in the treatment of cancer patients has a negative impact on patient survival. At present, Taiwan's radiotherapy center for radiotherapy schedules to stay in the pen and paper scheduling distribution, rarely optimized for this problem at the hospital, but also a lack of a set of radiotherapy centers in Taiwan meet the characteristics of the radiotherapy center.
This study applied the heuristic algorithm to patient scheduling problems with wall-controlled radiotherapy. Considering the constraint of the capacities of machines, the types of patient and machine and the acceptable waiting time for patients, a new mathematical model was proposed to schedule the daily radiotherapy patient scheduling. Furthermore, data were used to evaluate the performance of heuristic genetic algorithm (HGA) based on experimental design and response surface method. This study integrated theoretical research and practical application to develop a heuristic GA for radiotherapy patient scheduling problem. Hence, the efficiency could be improved, shorten the patient waiting time, balance the workload of each machine, and then improve the effective utilization of the device, which would ultimately improve the radiotherapy patient comfort and reduce cancer mortality.

摘要 I
ABSTRACT II
致謝 III
Contents IV
List of Tables VI
List of Figures VII
Chapter 1: Introduction 1
1.1 Background 1
1.2 Motivation 4
1.3 Organization of Thesis 6
Chapter 2: Literature Review 9
2.1 Patients Scheduling Problem 9
2.2 Radiotherapy Patient Scheduling Problem 10
2.3 Genetic Algorithm 13
Chapter 3: Problem Definition 17
3.1 Problem Statement 17
3.2 Notations and Assumptions 21
3.3 Problem Formulation 23
Chapter 4: Methodology 28
4.1 Algorithm Framework 28
4.2 Steps of Hybrid Genetic Algorithm 30
4.2.1 Set parameters 30
4.2.2 Chromosome representation 30
4.2.3 Initial population generation 31
4.2.4 Unfeasible chromosomes repairing mechanism 31
4.2.5 Selection operator 33
4.2.6 Generation replacement 33
4.2.7 Crossover operator 34
4.2.8 Mutation operator 35
4.2.9 Local search 36
4.2.10 Fuzzy logic controller 37
4.2.11 Termination 41
Chapter 5: Computational Study 42
5.1 Scenario Illustration 42
5.2 Comparison Results of LINGO and HGA 44
5.3 Comparison Results of Different Types GA 47
5.3.1 Objective value comparison 49
5.3.2 Runtime comparison 55
5.3.3 Convergence condition comparison 56
5.4 Comparison the Influence of Each Factor 59
5.5 Comparison Results of Present situation and HGA 62
Chapter 6: Conclusion 63
Reference 65

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