An Improved Genetic Algorithm for Fleet Assignment and Maintenance Routing Problems
碩士 === 國立成功大學 === 民航研究所 === 106 === 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 fi...
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ndltd-TW-106NCKU52940022019-05-16T01:07:58Z http://ndltd.ncl.edu.tw/handle/7dnam4 An Improved Genetic Algorithm for Fleet Assignment and Maintenance Routing Problems 應用改良之基因演算法於機隊規劃與維修排程問題 Yu-HsuanChang 張育瑄 碩士 國立成功大學 民航研究所 106 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. Ta-chung Wang 王大中 2018 學位論文 ; thesis 77 en_US |
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碩士 === 國立成功大學 === 民航研究所 === 106 === 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.
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Ta-chung Wang |
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Ta-chung Wang Yu-HsuanChang 張育瑄 |
author |
Yu-HsuanChang 張育瑄 |
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Yu-HsuanChang 張育瑄 An Improved Genetic Algorithm for Fleet Assignment and Maintenance Routing Problems |
author_sort |
Yu-HsuanChang |
title |
An Improved Genetic Algorithm for Fleet Assignment and Maintenance Routing Problems |
title_short |
An Improved Genetic Algorithm for Fleet Assignment and Maintenance Routing Problems |
title_full |
An Improved Genetic Algorithm for Fleet Assignment and Maintenance Routing Problems |
title_fullStr |
An Improved Genetic Algorithm for Fleet Assignment and Maintenance Routing Problems |
title_full_unstemmed |
An Improved Genetic Algorithm for Fleet Assignment and Maintenance Routing Problems |
title_sort |
improved genetic algorithm for fleet assignment and maintenance routing problems |
publishDate |
2018 |
url |
http://ndltd.ncl.edu.tw/handle/7dnam4 |
work_keys_str_mv |
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