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系統識別號 U0026-0207201814495000
論文名稱(中文) 應用改良之基因演算法於機隊規劃與維修排程問題
論文名稱(英文) An Improved Genetic Algorithm for Fleet Assignment and Maintenance Routing Problems
校院名稱 成功大學
系所名稱(中) 民航研究所
系所名稱(英) Institute of Civil Aviation
學年度 106
學期 2
出版年 107
研究生(中文) 張育瑄
研究生(英文) Yu-Hsuan Chang
學號 Q46054025
學位類別 碩士
語文別 英文
論文頁數 77頁
口試委員 指導教授-王大中
口試委員-詹劭勳
口試委員-林東盈
中文關鍵字 雜交  基因演算法  機隊規劃  航機維修排程 
英文關鍵字 crossover  Genetic Algorithm  aircraft routing  fleet assignment 
學科別分類
中文摘要 航班的設計規劃在航空公司營運上是非常重要的一個環節,其中包含許多決策因子和變數,像是航班的銜接、航機的指派、航機定期維修的需求和機組人員的排班等等。由於每項決策彼此都有緊密的關聯,而導致整個航班的設計規劃問題變得相當的龐大且複雜。面對這樣棘手的問題,早期的研究將其分割成五個具有邏輯順序的子問題,分別是:制訂航線、航班規劃、機隊指派、維修排程和機組人員排班,並求得各自的區域最佳解。本篇研究著重於整合機隊規劃與航機維修排程問題,目標為考慮不同性能的航機在符合定期維修的要求下達到適當的使用率,同時最大化獲利。研究方法的部分則是由一部份的整數規劃混和改良過的基因演算法,其中,染色體由指派給每架航機的航班串連而形成,並在應用特殊設計的基因雜交及突變步驟的情況下,順利的求得逼近全域最佳的解。本篇研究使用航空公司的資料來進行結果模擬以呈現所提出的研究方法。
英文摘要 Airline scheduling consists of several decision-making tasks. The entire problem scale is large and these decision tasks are closely related. This makes optimally solving the entire problem a challenging problem. Former researchers have divided the problem into five sub-problems which could be solved sequentially while sacrificing the global optimality. The ordering of these five sub-problems are route development, flight scheduling, fleet assignment problem (FAP), aircraft maintenance routing problem (AMRP), and crew scheduling. In this study, we aim to deal with a problem of solving FAP and AMRP simultaneously. A specially designed GA and integer linear programming are combined for such problem. In the improved GA, the chromosomes are arranged to represent the flight paths for each aircraft. New crossover and mutation processes can then be conducted in a more effective way to finding near-optimal solutions. Real data from airlines are used to demonstrate the effectiveness of the improved method.
論文目次 摘要 I
Abstract II
誌謝 III
Contents IV
List of Tables VI
List of Figures VII
Nomenclature IX
Acronyms XII
Chapter 1 Introduction 1
1.1 Motivation and Objective 1
1.2 Literature Review 5
1.3 Outline of this Thesis 10
Chapter 2 Integration of Fleet Assignment and Aircraft Maintenance Routing Problem 11
2.1 Fleet Assignment Problem (FAP) 11
2.1.1 Networks for FAP 11
2.2 Aircraft Maintenance Routing Problem (AMRP) 17
2.2.1 Heuristic Method for AMRP 19
2.3 Fleet Assignment and Maintenance Routing Problem (FAMRP) 24
2.3.1 Integer Linear Programming Model for FAMRP 28
Chapter 3 Genetic Algorithm for FAMRP 31
3.1 General Genetic Algorithm 31
3.1.1 The Basic Structure of a Genetic Algorithm 32
3.2 Improved Genetic Algorithm for FAMRP 42
3.3 The Mixture of Integer Linear Program and Improved GA 57
Chapter 4 Simulation Results 58
4.1 Simulation Data and Environment 58
4.2 Parameters Decision 61
4.3 Simulation Result 62
Chapter 5 Conclusions and Future Research 73
5.1 Conclusion 73
5.2 Future Research 74
References 75

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