The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
pandangan teks lengkap
82
Penjadualan pendaratan pesawat (ALS) adalah salah satu cabaran terpenting dalam pengurusan trafik udara. Sasaran ALS adalah untuk memutuskan urutan penjadualan pendaratan dan mengira masa pendaratan untuk setiap pesawat di kawasan terminal. Masa pendaratan ini adalah dalam tingkap masa, dan jarak pemisahan keselamatan antara pesawat mesti dikekalkan. ALS adalah masalah yang kompleks, terutamanya dengan sejumlah besar pesawat. Dalam kajian ini, kami mencadangkan heuristik novel yang dipanggil CGIC untuk menyelesaikan masalah ALS. CGIC terdiri daripada empat komponen: peraturan chunking berdasarkan kos, peraturan penjanaan jujukan pendaratan, heuristik peningkatan bongkah dan peraturan sambungan. Dalam algoritma ini, kami mengurangkan kerumitan masalah ALS dengan memecahkannya kepada dua atau lebih submasalah dengan kurang pesawat. Pertama, jujukan pendaratan yang boleh dilaksanakan dijana dan dibahagikan kepada beberapa jujukan sebagai ketulan mengikut peraturan chunking berdasarkan kos pesawat. Kedua, setiap bongkah dijana semula oleh heuristik konstruktif, dan heuristik perturbatif digunakan untuk menambah baik bongkah. Akhirnya, semua ketulan membentuk urutan pendaratan yang boleh dilaksanakan melalui peraturan sambungan, dan masa pendaratan setiap pesawat dikira berdasarkan urutan ini. Simulasi menunjukkan bahawa (a) peraturan chunking berdasarkan kos mengatasi peraturan chunking lain berdasarkan masa atau berat untuk ALS dalam keadaan statik, yang mempunyai bilangan pesawat yang banyak; (b) CGIC yang dicadangkan boleh menyelesaikan masalah ALS sehingga 500 pesawat secara optimum; (c) dalam keadaan dinamik, CGIC boleh mendapatkan penyelesaian berkualiti tinggi, dan masa pengiraan CGIC adalah cukup rendah untuk membolehkan pelaksanaan masa nyata.
Wen SHI
the Tianjin University of Commerce
Shan JIANG
the Tianjin Medical University
Xuan LIANG
the Tianjin University of Commerce
Na ZHOU
the Tianjin University of Commerce
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Salinan
Wen SHI, Shan JIANG, Xuan LIANG, Na ZHOU, "A Heuristic Algorithm for Solving the Aircraft Landing Scheduling Problem with a Landing Sequence Division" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 8, pp. 966-973, August 2019, doi: 10.1587/transfun.E102.A.966.
Abstract: Aircraft landing scheduling (ALS) is one of the most important challenges in air traffic management. The target of ALS is to decide a landing scheduling sequence and calculate a landing time for each aircraft in terminal areas. These landing times are within time windows, and safety separation distances between aircraft must be kept. ALS is a complex problem, especially with a large number of aircraft. In this study, we propose a novel heuristic called CGIC to solve ALS problems. The CGIC consists of four components: a chunking rule based on costs, a landing subsequence generation rule, a chunk improvement heuristic, and a connection rule. In this algorithm, we reduce the complexity of the ALS problem by breaking it down into two or more subproblems with less aircraft. First, a feasible landing sequence is generated and divided into several subsequences as chunks by a chunking rule based on aircraft cost. Second, each chunk is regenerated by a constructive heuristic, and a perturbative heuristic is applied to improve the chunks. Finally, all chunks constitute a feasible landing sequence through a connection rule, and the landing time of each aircraft is calculated on the basis of this sequence. Simulations demonstrate that (a) the chunking rule based on cost outperforms other chunking rules based on time or weight for ALS in static instances, which have a large number of aircraft; (b) the proposed CGIC can solve the ALS problem up to 500 aircraft optimally; (c) in dynamic instances, CGIC can obtain high-quality solutions, and the computation time of CGIC is low enough to enable real-time execution.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.966/_p
Salinan
@ARTICLE{e102-a_8_966,
author={Wen SHI, Shan JIANG, Xuan LIANG, Na ZHOU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Heuristic Algorithm for Solving the Aircraft Landing Scheduling Problem with a Landing Sequence Division},
year={2019},
volume={E102-A},
number={8},
pages={966-973},
abstract={Aircraft landing scheduling (ALS) is one of the most important challenges in air traffic management. The target of ALS is to decide a landing scheduling sequence and calculate a landing time for each aircraft in terminal areas. These landing times are within time windows, and safety separation distances between aircraft must be kept. ALS is a complex problem, especially with a large number of aircraft. In this study, we propose a novel heuristic called CGIC to solve ALS problems. The CGIC consists of four components: a chunking rule based on costs, a landing subsequence generation rule, a chunk improvement heuristic, and a connection rule. In this algorithm, we reduce the complexity of the ALS problem by breaking it down into two or more subproblems with less aircraft. First, a feasible landing sequence is generated and divided into several subsequences as chunks by a chunking rule based on aircraft cost. Second, each chunk is regenerated by a constructive heuristic, and a perturbative heuristic is applied to improve the chunks. Finally, all chunks constitute a feasible landing sequence through a connection rule, and the landing time of each aircraft is calculated on the basis of this sequence. Simulations demonstrate that (a) the chunking rule based on cost outperforms other chunking rules based on time or weight for ALS in static instances, which have a large number of aircraft; (b) the proposed CGIC can solve the ALS problem up to 500 aircraft optimally; (c) in dynamic instances, CGIC can obtain high-quality solutions, and the computation time of CGIC is low enough to enable real-time execution.},
keywords={},
doi={10.1587/transfun.E102.A.966},
ISSN={1745-1337},
month={August},}
Salinan
TY - JOUR
TI - A Heuristic Algorithm for Solving the Aircraft Landing Scheduling Problem with a Landing Sequence Division
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 966
EP - 973
AU - Wen SHI
AU - Shan JIANG
AU - Xuan LIANG
AU - Na ZHOU
PY - 2019
DO - 10.1587/transfun.E102.A.966
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2019
AB - Aircraft landing scheduling (ALS) is one of the most important challenges in air traffic management. The target of ALS is to decide a landing scheduling sequence and calculate a landing time for each aircraft in terminal areas. These landing times are within time windows, and safety separation distances between aircraft must be kept. ALS is a complex problem, especially with a large number of aircraft. In this study, we propose a novel heuristic called CGIC to solve ALS problems. The CGIC consists of four components: a chunking rule based on costs, a landing subsequence generation rule, a chunk improvement heuristic, and a connection rule. In this algorithm, we reduce the complexity of the ALS problem by breaking it down into two or more subproblems with less aircraft. First, a feasible landing sequence is generated and divided into several subsequences as chunks by a chunking rule based on aircraft cost. Second, each chunk is regenerated by a constructive heuristic, and a perturbative heuristic is applied to improve the chunks. Finally, all chunks constitute a feasible landing sequence through a connection rule, and the landing time of each aircraft is calculated on the basis of this sequence. Simulations demonstrate that (a) the chunking rule based on cost outperforms other chunking rules based on time or weight for ALS in static instances, which have a large number of aircraft; (b) the proposed CGIC can solve the ALS problem up to 500 aircraft optimally; (c) in dynamic instances, CGIC can obtain high-quality solutions, and the computation time of CGIC is low enough to enable real-time execution.
ER -