Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions
Abstract Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likeli...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Radzki, G. [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021 |
|---|
| Schlagwörter: |
Unmanned aerial vehicles routing |
|---|
| Anmerkung: |
© The Author(s) 2021 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of intelligent & robotic systems - Springer Netherlands, 1988, 103(2021), 4 vom: 10. Nov. |
|---|---|
| Übergeordnetes Werk: |
volume:103 ; year:2021 ; number:4 ; day:10 ; month:11 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10846-021-01502-2 |
|---|
| Katalog-ID: |
OLC2077401389 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2077401389 | ||
| 003 | DE-627 | ||
| 005 | 20230505185359.0 | ||
| 007 | tu | ||
| 008 | 221220s2021 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10846-021-01502-2 |2 doi | |
| 035 | |a (DE-627)OLC2077401389 | ||
| 035 | |a (DE-He213)s10846-021-01502-2-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 100 | 1 | |a Radzki, G. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © The Author(s) 2021 | ||
| 520 | |a Abstract Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout. | ||
| 650 | 4 | |a Unmanned aerial vehicles routing | |
| 650 | 4 | |a Unmanned aerial vehicles scheduling | |
| 650 | 4 | |a Re-routing | |
| 650 | 4 | |a Rescheduling | |
| 650 | 4 | |a UAV fleet mission planning | |
| 650 | 4 | |a Robust planning | |
| 700 | 1 | |a Golinska-Dawson, P. |0 (orcid)0000-0002-5821-3805 |4 aut | |
| 700 | 1 | |a Bocewicz, G. |4 aut | |
| 700 | 1 | |a Banaszak, Z. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of intelligent & robotic systems |d Springer Netherlands, 1988 |g 103(2021), 4 vom: 10. Nov. |w (DE-627)130464864 |w (DE-600)740594-7 |w (DE-576)018667805 |x 0921-0296 |7 nnns |
| 773 | 1 | 8 | |g volume:103 |g year:2021 |g number:4 |g day:10 |g month:11 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s10846-021-01502-2 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 951 | |a AR | ||
| 952 | |d 103 |j 2021 |e 4 |b 10 |c 11 | ||
| author_variant |
g r gr p g d pgd g b gb z b zb |
|---|---|
| matchkey_str |
article:09210296:2021----::oelnrbsdlvrseaisoaleoumnearavhcei |
| hierarchy_sort_str |
2021 |
| publishDate |
2021 |
| allfields |
10.1007/s10846-021-01502-2 doi (DE-627)OLC2077401389 (DE-He213)s10846-021-01502-2-p DE-627 ger DE-627 rakwb eng 004 VZ Radzki, G. verfasserin aut Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout. Unmanned aerial vehicles routing Unmanned aerial vehicles scheduling Re-routing Rescheduling UAV fleet mission planning Robust planning Golinska-Dawson, P. (orcid)0000-0002-5821-3805 aut Bocewicz, G. aut Banaszak, Z. aut Enthalten in Journal of intelligent & robotic systems Springer Netherlands, 1988 103(2021), 4 vom: 10. Nov. (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:103 year:2021 number:4 day:10 month:11 https://doi.org/10.1007/s10846-021-01502-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 103 2021 4 10 11 |
| spelling |
10.1007/s10846-021-01502-2 doi (DE-627)OLC2077401389 (DE-He213)s10846-021-01502-2-p DE-627 ger DE-627 rakwb eng 004 VZ Radzki, G. verfasserin aut Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout. Unmanned aerial vehicles routing Unmanned aerial vehicles scheduling Re-routing Rescheduling UAV fleet mission planning Robust planning Golinska-Dawson, P. (orcid)0000-0002-5821-3805 aut Bocewicz, G. aut Banaszak, Z. aut Enthalten in Journal of intelligent & robotic systems Springer Netherlands, 1988 103(2021), 4 vom: 10. Nov. (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:103 year:2021 number:4 day:10 month:11 https://doi.org/10.1007/s10846-021-01502-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 103 2021 4 10 11 |
| allfields_unstemmed |
10.1007/s10846-021-01502-2 doi (DE-627)OLC2077401389 (DE-He213)s10846-021-01502-2-p DE-627 ger DE-627 rakwb eng 004 VZ Radzki, G. verfasserin aut Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout. Unmanned aerial vehicles routing Unmanned aerial vehicles scheduling Re-routing Rescheduling UAV fleet mission planning Robust planning Golinska-Dawson, P. (orcid)0000-0002-5821-3805 aut Bocewicz, G. aut Banaszak, Z. aut Enthalten in Journal of intelligent & robotic systems Springer Netherlands, 1988 103(2021), 4 vom: 10. Nov. (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:103 year:2021 number:4 day:10 month:11 https://doi.org/10.1007/s10846-021-01502-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 103 2021 4 10 11 |
| allfieldsGer |
10.1007/s10846-021-01502-2 doi (DE-627)OLC2077401389 (DE-He213)s10846-021-01502-2-p DE-627 ger DE-627 rakwb eng 004 VZ Radzki, G. verfasserin aut Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout. Unmanned aerial vehicles routing Unmanned aerial vehicles scheduling Re-routing Rescheduling UAV fleet mission planning Robust planning Golinska-Dawson, P. (orcid)0000-0002-5821-3805 aut Bocewicz, G. aut Banaszak, Z. aut Enthalten in Journal of intelligent & robotic systems Springer Netherlands, 1988 103(2021), 4 vom: 10. Nov. (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:103 year:2021 number:4 day:10 month:11 https://doi.org/10.1007/s10846-021-01502-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 103 2021 4 10 11 |
| allfieldsSound |
10.1007/s10846-021-01502-2 doi (DE-627)OLC2077401389 (DE-He213)s10846-021-01502-2-p DE-627 ger DE-627 rakwb eng 004 VZ Radzki, G. verfasserin aut Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout. Unmanned aerial vehicles routing Unmanned aerial vehicles scheduling Re-routing Rescheduling UAV fleet mission planning Robust planning Golinska-Dawson, P. (orcid)0000-0002-5821-3805 aut Bocewicz, G. aut Banaszak, Z. aut Enthalten in Journal of intelligent & robotic systems Springer Netherlands, 1988 103(2021), 4 vom: 10. Nov. (DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 0921-0296 nnns volume:103 year:2021 number:4 day:10 month:11 https://doi.org/10.1007/s10846-021-01502-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 103 2021 4 10 11 |
| language |
English |
| source |
Enthalten in Journal of intelligent & robotic systems 103(2021), 4 vom: 10. Nov. volume:103 year:2021 number:4 day:10 month:11 |
| sourceStr |
Enthalten in Journal of intelligent & robotic systems 103(2021), 4 vom: 10. Nov. volume:103 year:2021 number:4 day:10 month:11 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Unmanned aerial vehicles routing Unmanned aerial vehicles scheduling Re-routing Rescheduling UAV fleet mission planning Robust planning |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Journal of intelligent & robotic systems |
| authorswithroles_txt_mv |
Radzki, G. @@aut@@ Golinska-Dawson, P. @@aut@@ Bocewicz, G. @@aut@@ Banaszak, Z. @@aut@@ |
| publishDateDaySort_date |
2021-11-10T00:00:00Z |
| hierarchy_top_id |
130464864 |
| dewey-sort |
14 |
| id |
OLC2077401389 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2077401389</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505185359.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">221220s2021 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10846-021-01502-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2077401389</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10846-021-01502-2-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Radzki, G.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Unmanned aerial vehicles routing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Unmanned aerial vehicles scheduling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Re-routing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Rescheduling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">UAV fleet mission planning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Robust planning</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Golinska-Dawson, P.</subfield><subfield code="0">(orcid)0000-0002-5821-3805</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bocewicz, G.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Banaszak, Z.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of intelligent & robotic systems</subfield><subfield code="d">Springer Netherlands, 1988</subfield><subfield code="g">103(2021), 4 vom: 10. Nov.</subfield><subfield code="w">(DE-627)130464864</subfield><subfield code="w">(DE-600)740594-7</subfield><subfield code="w">(DE-576)018667805</subfield><subfield code="x">0921-0296</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:103</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:4</subfield><subfield code="g">day:10</subfield><subfield code="g">month:11</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10846-021-01502-2</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">103</subfield><subfield code="j">2021</subfield><subfield code="e">4</subfield><subfield code="b">10</subfield><subfield code="c">11</subfield></datafield></record></collection>
|
| author |
Radzki, G. |
| spellingShingle |
Radzki, G. ddc 004 misc Unmanned aerial vehicles routing misc Unmanned aerial vehicles scheduling misc Re-routing misc Rescheduling misc UAV fleet mission planning misc Robust planning Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions |
| authorStr |
Radzki, G. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)130464864 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0921-0296 |
| topic_title |
004 VZ Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions Unmanned aerial vehicles routing Unmanned aerial vehicles scheduling Re-routing Rescheduling UAV fleet mission planning Robust planning |
| topic |
ddc 004 misc Unmanned aerial vehicles routing misc Unmanned aerial vehicles scheduling misc Re-routing misc Rescheduling misc UAV fleet mission planning misc Robust planning |
| topic_unstemmed |
ddc 004 misc Unmanned aerial vehicles routing misc Unmanned aerial vehicles scheduling misc Re-routing misc Rescheduling misc UAV fleet mission planning misc Robust planning |
| topic_browse |
ddc 004 misc Unmanned aerial vehicles routing misc Unmanned aerial vehicles scheduling misc Re-routing misc Rescheduling misc UAV fleet mission planning misc Robust planning |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Journal of intelligent & robotic systems |
| hierarchy_parent_id |
130464864 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Journal of intelligent & robotic systems |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)130464864 (DE-600)740594-7 (DE-576)018667805 |
| title |
Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions |
| ctrlnum |
(DE-627)OLC2077401389 (DE-He213)s10846-021-01502-2-p |
| title_full |
Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions |
| author_sort |
Radzki, G. |
| journal |
Journal of intelligent & robotic systems |
| journalStr |
Journal of intelligent & robotic systems |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2021 |
| contenttype_str_mv |
txt |
| author_browse |
Radzki, G. Golinska-Dawson, P. Bocewicz, G. Banaszak, Z. |
| container_volume |
103 |
| class |
004 VZ |
| format_se |
Aufsätze |
| author-letter |
Radzki, G. |
| doi_str_mv |
10.1007/s10846-021-01502-2 |
| normlink |
(ORCID)0000-0002-5821-3805 |
| normlink_prefix_str_mv |
(orcid)0000-0002-5821-3805 |
| dewey-full |
004 |
| title_sort |
modelling robust delivery scenarios for a fleet of unmanned aerial vehicles in disaster relief missions |
| title_auth |
Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions |
| abstract |
Abstract Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout. © The Author(s) 2021 |
| abstractGer |
Abstract Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout. © The Author(s) 2021 |
| abstract_unstemmed |
Abstract Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout. © The Author(s) 2021 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT |
| container_issue |
4 |
| title_short |
Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions |
| url |
https://doi.org/10.1007/s10846-021-01502-2 |
| remote_bool |
false |
| author2 |
Golinska-Dawson, P. Bocewicz, G. Banaszak, Z. |
| author2Str |
Golinska-Dawson, P. Bocewicz, G. Banaszak, Z. |
| ppnlink |
130464864 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10846-021-01502-2 |
| up_date |
2024-07-03T15:21:19.956Z |
| _version_ |
1803571775363088384 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2077401389</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505185359.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">221220s2021 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10846-021-01502-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2077401389</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10846-021-01502-2-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Radzki, G.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Unmanned aerial vehicles routing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Unmanned aerial vehicles scheduling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Re-routing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Rescheduling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">UAV fleet mission planning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Robust planning</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Golinska-Dawson, P.</subfield><subfield code="0">(orcid)0000-0002-5821-3805</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bocewicz, G.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Banaszak, Z.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of intelligent & robotic systems</subfield><subfield code="d">Springer Netherlands, 1988</subfield><subfield code="g">103(2021), 4 vom: 10. Nov.</subfield><subfield code="w">(DE-627)130464864</subfield><subfield code="w">(DE-600)740594-7</subfield><subfield code="w">(DE-576)018667805</subfield><subfield code="x">0921-0296</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:103</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:4</subfield><subfield code="g">day:10</subfield><subfield code="g">month:11</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10846-021-01502-2</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">103</subfield><subfield code="j">2021</subfield><subfield code="e">4</subfield><subfield code="b">10</subfield><subfield code="c">11</subfield></datafield></record></collection>
|
| score |
7.4013214 |