UAV mission scheduling with completion time, flight distance, and resource consumption constraints

Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint,...
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Autor*in:	Keqin Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Algorithmic framework combinatorial optimisation heterogeneous UAVs heuristic algorithm mission scheduling

Übergeordnetes Werk:	In: Connection Science - Taylor & Francis Group, 2022, 35(2023), 1
Übergeordnetes Werk:	volume:35 ; year:2023 ; number:1

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1080/09540091.2023.2281250

Katalog-ID:	DOAJ095424679

Internformat


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publishDate	2023
allfields	10.1080/09540091.2023.2281250 doi (DE-627)DOAJ095424679 (DE-599)DOAJa7399e6a4fdc4ff9a5639967c587bdd3 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Keqin Li verfasserin aut UAV mission scheduling with completion time, flight distance, and resource consumption constraints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems. Algorithmic framework combinatorial optimisation heterogeneous UAVs heuristic algorithm mission scheduling Electronic computers. Computer science In Connection Science Taylor & Francis Group, 2022 35(2023), 1 (DE-627)306713276 (DE-600)1501040-5 13600494 nnns volume:35 year:2023 number:1 https://doi.org/10.1080/09540091.2023.2281250 kostenfrei https://doaj.org/article/a7399e6a4fdc4ff9a5639967c587bdd3 kostenfrei https://www.tandfonline.com/doi/10.1080/09540091.2023.2281250 kostenfrei https://doaj.org/toc/0954-0091 Journal toc kostenfrei https://doaj.org/toc/1360-0494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 35 2023 1
spelling	10.1080/09540091.2023.2281250 doi (DE-627)DOAJ095424679 (DE-599)DOAJa7399e6a4fdc4ff9a5639967c587bdd3 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Keqin Li verfasserin aut UAV mission scheduling with completion time, flight distance, and resource consumption constraints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems. Algorithmic framework combinatorial optimisation heterogeneous UAVs heuristic algorithm mission scheduling Electronic computers. Computer science In Connection Science Taylor & Francis Group, 2022 35(2023), 1 (DE-627)306713276 (DE-600)1501040-5 13600494 nnns volume:35 year:2023 number:1 https://doi.org/10.1080/09540091.2023.2281250 kostenfrei https://doaj.org/article/a7399e6a4fdc4ff9a5639967c587bdd3 kostenfrei https://www.tandfonline.com/doi/10.1080/09540091.2023.2281250 kostenfrei https://doaj.org/toc/0954-0091 Journal toc kostenfrei https://doaj.org/toc/1360-0494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 35 2023 1
allfields_unstemmed	10.1080/09540091.2023.2281250 doi (DE-627)DOAJ095424679 (DE-599)DOAJa7399e6a4fdc4ff9a5639967c587bdd3 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Keqin Li verfasserin aut UAV mission scheduling with completion time, flight distance, and resource consumption constraints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems. Algorithmic framework combinatorial optimisation heterogeneous UAVs heuristic algorithm mission scheduling Electronic computers. Computer science In Connection Science Taylor & Francis Group, 2022 35(2023), 1 (DE-627)306713276 (DE-600)1501040-5 13600494 nnns volume:35 year:2023 number:1 https://doi.org/10.1080/09540091.2023.2281250 kostenfrei https://doaj.org/article/a7399e6a4fdc4ff9a5639967c587bdd3 kostenfrei https://www.tandfonline.com/doi/10.1080/09540091.2023.2281250 kostenfrei https://doaj.org/toc/0954-0091 Journal toc kostenfrei https://doaj.org/toc/1360-0494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 35 2023 1
allfieldsGer	10.1080/09540091.2023.2281250 doi (DE-627)DOAJ095424679 (DE-599)DOAJa7399e6a4fdc4ff9a5639967c587bdd3 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Keqin Li verfasserin aut UAV mission scheduling with completion time, flight distance, and resource consumption constraints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems. Algorithmic framework combinatorial optimisation heterogeneous UAVs heuristic algorithm mission scheduling Electronic computers. Computer science In Connection Science Taylor & Francis Group, 2022 35(2023), 1 (DE-627)306713276 (DE-600)1501040-5 13600494 nnns volume:35 year:2023 number:1 https://doi.org/10.1080/09540091.2023.2281250 kostenfrei https://doaj.org/article/a7399e6a4fdc4ff9a5639967c587bdd3 kostenfrei https://www.tandfonline.com/doi/10.1080/09540091.2023.2281250 kostenfrei https://doaj.org/toc/0954-0091 Journal toc kostenfrei https://doaj.org/toc/1360-0494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 35 2023 1
allfieldsSound	10.1080/09540091.2023.2281250 doi (DE-627)DOAJ095424679 (DE-599)DOAJa7399e6a4fdc4ff9a5639967c587bdd3 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Keqin Li verfasserin aut UAV mission scheduling with completion time, flight distance, and resource consumption constraints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems. Algorithmic framework combinatorial optimisation heterogeneous UAVs heuristic algorithm mission scheduling Electronic computers. Computer science In Connection Science Taylor & Francis Group, 2022 35(2023), 1 (DE-627)306713276 (DE-600)1501040-5 13600494 nnns volume:35 year:2023 number:1 https://doi.org/10.1080/09540091.2023.2281250 kostenfrei https://doaj.org/article/a7399e6a4fdc4ff9a5639967c587bdd3 kostenfrei https://www.tandfonline.com/doi/10.1080/09540091.2023.2281250 kostenfrei https://doaj.org/toc/0954-0091 Journal toc kostenfrei https://doaj.org/toc/1360-0494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 35 2023 1
language	English
source	In Connection Science 35(2023), 1 volume:35 year:2023 number:1
sourceStr	In Connection Science 35(2023), 1 volume:35 year:2023 number:1
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Algorithmic framework combinatorial optimisation heterogeneous UAVs heuristic algorithm mission scheduling Electronic computers. Computer science
isfreeaccess_bool	true
container_title	Connection Science
authorswithroles_txt_mv	Keqin Li @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	306713276
id	DOAJ095424679
language_de	englisch
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callnumber-first	Q - Science
author	Keqin Li
spellingShingle	Keqin Li misc QA75.5-76.95 misc Algorithmic framework misc combinatorial optimisation misc heterogeneous UAVs misc heuristic algorithm misc mission scheduling misc Electronic computers. Computer science UAV mission scheduling with completion time, flight distance, and resource consumption constraints
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topic_title	QA75.5-76.95 UAV mission scheduling with completion time, flight distance, and resource consumption constraints Algorithmic framework combinatorial optimisation heterogeneous UAVs heuristic algorithm mission scheduling
topic	misc QA75.5-76.95 misc Algorithmic framework misc combinatorial optimisation misc heterogeneous UAVs misc heuristic algorithm misc mission scheduling misc Electronic computers. Computer science
topic_unstemmed	misc QA75.5-76.95 misc Algorithmic framework misc combinatorial optimisation misc heterogeneous UAVs misc heuristic algorithm misc mission scheduling misc Electronic computers. Computer science
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title	UAV mission scheduling with completion time, flight distance, and resource consumption constraints
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title_full	UAV mission scheduling with completion time, flight distance, and resource consumption constraints
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title_sort	uav mission scheduling with completion time, flight distance, and resource consumption constraints
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title_auth	UAV mission scheduling with completion time, flight distance, and resource consumption constraints
abstract	Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems.
abstractGer	Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems.
abstract_unstemmed	Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems.
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title_short	UAV mission scheduling with completion time, flight distance, and resource consumption constraints
url	https://doi.org/10.1080/09540091.2023.2281250 https://doaj.org/article/a7399e6a4fdc4ff9a5639967c587bdd3 https://www.tandfonline.com/doi/10.1080/09540091.2023.2281250 https://doaj.org/toc/0954-0091 https://doaj.org/toc/1360-0494
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UAV mission scheduling with completion time, flight distance, and resource consumption constraints

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