Planning robust drone-truck delivery routes under road traffic uncertainty

In this paper, we show the potential of a drone-truck logistics system to provide fast last-mile delivery services. In the system, a truck and a drone work in tandem to serve customers within pre-specified delivery time windows. Since the uncertainty in the ground traffic network can not only fail a...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yang, Yu [verfasserIn] Yan, Chiwei [verfasserIn] Cao, Yufeng [verfasserIn] Roberti, Roberto [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Transportation Last-mile delivery Drone Robust optimization Branch-and-price

Übergeordnetes Werk:	Enthalten in: European journal of operational research - Amsterdam [u.a.] : Elsevier, 1977, 309, Seite 1145-1160
Übergeordnetes Werk:	volume:309 ; pages:1145-1160

DOI / URN:	10.1016/j.ejor.2023.02.031

Katalog-ID:	ELV063005166

Internformat


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100	1		\|a Yang, Yu \|e verfasserin \|0 (orcid)0000-0002-0502-7603 \|4 aut
245	1	0	\|a Planning robust drone-truck delivery routes under road traffic uncertainty
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520			\|a In this paper, we show the potential of a drone-truck logistics system to provide fast last-mile delivery services. In the system, a truck and a drone work in tandem to serve customers within pre-specified delivery time windows. Since the uncertainty in the ground traffic network can not only fail a service promise but also expose the drone to danger, we focus on mitigating such risks when designing the routing plan. In particular, we investigate the robust drone-truck delivery problem (RDTDP) that seeks a robust joint route for the truck-and-drone tandem to maximize the profit. We develop an exact branch-and-price (B&P) solution approach that can solve RDTDP instances, both randomly generated and collected from real-life data, with up to 40 service requests. In a numerical study, we demonstrate that the solution obtained with our proposed B&P approach is significantly more robust than the one obtained without considering any uncertainty. In particular, while maintaining a comparable mean value in the solution quality measures, the robust solution features a variance up to 58% smaller and a feasibility ratio (i.e., on-time performance) up to 90% higher than the deterministic solution. These insights suggest that the robust route can be carried out much more frequently in practical usage.
650		4	\|a Transportation
650		4	\|a Last-mile delivery
650		4	\|a Drone
650		4	\|a Robust optimization
650		4	\|a Branch-and-price
700	1		\|a Yan, Chiwei \|e verfasserin \|4 aut
700	1		\|a Cao, Yufeng \|e verfasserin \|0 (orcid)0000-0002-9183-0215 \|4 aut
700	1		\|a Roberti, Roberto \|e verfasserin \|0 (orcid)0000-0002-2987-1593 \|4 aut
773	0	8	\|i Enthalten in \|t European journal of operational research \|d Amsterdam [u.a.] : Elsevier, 1977 \|g 309, Seite 1145-1160 \|h Online-Ressource \|w (DE-627)306713470 \|w (DE-600)1501061-2 \|w (DE-576)094058377 \|x 0377-2217 \|7 nnns
773	1	8	\|g volume:309 \|g pages:1145-1160
912			\|a GBV_USEFLAG_U
912			\|a GBV_ELV
912			\|a SYSFLAG_U
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912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 85.03 \|j Methoden und Techniken der Betriebswirtschaft \|q VZ
951			\|a AR
952			\|d 309 \|h 1145-1160

Indexfelder

author_variant	y y yy c y cy y c yc r r rr
matchkey_str	article:03772217:2023----::lnigoutrntukeieyotsnerat
hierarchy_sort_str	2023
bklnumber	85.03
publishDate	2023
allfields	10.1016/j.ejor.2023.02.031 doi (DE-627)ELV063005166 (ELSEVIER)S0377-2217(23)00164-9 DE-627 ger DE-627 rda eng 650 VZ 85.03 bkl Yang, Yu verfasserin (orcid)0000-0002-0502-7603 aut Planning robust drone-truck delivery routes under road traffic uncertainty 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we show the potential of a drone-truck logistics system to provide fast last-mile delivery services. In the system, a truck and a drone work in tandem to serve customers within pre-specified delivery time windows. Since the uncertainty in the ground traffic network can not only fail a service promise but also expose the drone to danger, we focus on mitigating such risks when designing the routing plan. In particular, we investigate the robust drone-truck delivery problem (RDTDP) that seeks a robust joint route for the truck-and-drone tandem to maximize the profit. We develop an exact branch-and-price (B&P) solution approach that can solve RDTDP instances, both randomly generated and collected from real-life data, with up to 40 service requests. In a numerical study, we demonstrate that the solution obtained with our proposed B&P approach is significantly more robust than the one obtained without considering any uncertainty. In particular, while maintaining a comparable mean value in the solution quality measures, the robust solution features a variance up to 58% smaller and a feasibility ratio (i.e., on-time performance) up to 90% higher than the deterministic solution. These insights suggest that the robust route can be carried out much more frequently in practical usage. Transportation Last-mile delivery Drone Robust optimization Branch-and-price Yan, Chiwei verfasserin aut Cao, Yufeng verfasserin (orcid)0000-0002-9183-0215 aut Roberti, Roberto verfasserin (orcid)0000-0002-2987-1593 aut Enthalten in European journal of operational research Amsterdam [u.a.] : Elsevier, 1977 309, Seite 1145-1160 Online-Ressource (DE-627)306713470 (DE-600)1501061-2 (DE-576)094058377 0377-2217 nnns volume:309 pages:1145-1160 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 Methoden und Techniken der Betriebswirtschaft VZ AR 309 1145-1160
spelling	10.1016/j.ejor.2023.02.031 doi (DE-627)ELV063005166 (ELSEVIER)S0377-2217(23)00164-9 DE-627 ger DE-627 rda eng 650 VZ 85.03 bkl Yang, Yu verfasserin (orcid)0000-0002-0502-7603 aut Planning robust drone-truck delivery routes under road traffic uncertainty 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we show the potential of a drone-truck logistics system to provide fast last-mile delivery services. In the system, a truck and a drone work in tandem to serve customers within pre-specified delivery time windows. Since the uncertainty in the ground traffic network can not only fail a service promise but also expose the drone to danger, we focus on mitigating such risks when designing the routing plan. In particular, we investigate the robust drone-truck delivery problem (RDTDP) that seeks a robust joint route for the truck-and-drone tandem to maximize the profit. We develop an exact branch-and-price (B&P) solution approach that can solve RDTDP instances, both randomly generated and collected from real-life data, with up to 40 service requests. In a numerical study, we demonstrate that the solution obtained with our proposed B&P approach is significantly more robust than the one obtained without considering any uncertainty. In particular, while maintaining a comparable mean value in the solution quality measures, the robust solution features a variance up to 58% smaller and a feasibility ratio (i.e., on-time performance) up to 90% higher than the deterministic solution. These insights suggest that the robust route can be carried out much more frequently in practical usage. Transportation Last-mile delivery Drone Robust optimization Branch-and-price Yan, Chiwei verfasserin aut Cao, Yufeng verfasserin (orcid)0000-0002-9183-0215 aut Roberti, Roberto verfasserin (orcid)0000-0002-2987-1593 aut Enthalten in European journal of operational research Amsterdam [u.a.] : Elsevier, 1977 309, Seite 1145-1160 Online-Ressource (DE-627)306713470 (DE-600)1501061-2 (DE-576)094058377 0377-2217 nnns volume:309 pages:1145-1160 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 Methoden und Techniken der Betriebswirtschaft VZ AR 309 1145-1160
allfields_unstemmed	10.1016/j.ejor.2023.02.031 doi (DE-627)ELV063005166 (ELSEVIER)S0377-2217(23)00164-9 DE-627 ger DE-627 rda eng 650 VZ 85.03 bkl Yang, Yu verfasserin (orcid)0000-0002-0502-7603 aut Planning robust drone-truck delivery routes under road traffic uncertainty 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we show the potential of a drone-truck logistics system to provide fast last-mile delivery services. In the system, a truck and a drone work in tandem to serve customers within pre-specified delivery time windows. Since the uncertainty in the ground traffic network can not only fail a service promise but also expose the drone to danger, we focus on mitigating such risks when designing the routing plan. In particular, we investigate the robust drone-truck delivery problem (RDTDP) that seeks a robust joint route for the truck-and-drone tandem to maximize the profit. We develop an exact branch-and-price (B&P) solution approach that can solve RDTDP instances, both randomly generated and collected from real-life data, with up to 40 service requests. In a numerical study, we demonstrate that the solution obtained with our proposed B&P approach is significantly more robust than the one obtained without considering any uncertainty. In particular, while maintaining a comparable mean value in the solution quality measures, the robust solution features a variance up to 58% smaller and a feasibility ratio (i.e., on-time performance) up to 90% higher than the deterministic solution. These insights suggest that the robust route can be carried out much more frequently in practical usage. Transportation Last-mile delivery Drone Robust optimization Branch-and-price Yan, Chiwei verfasserin aut Cao, Yufeng verfasserin (orcid)0000-0002-9183-0215 aut Roberti, Roberto verfasserin (orcid)0000-0002-2987-1593 aut Enthalten in European journal of operational research Amsterdam [u.a.] : Elsevier, 1977 309, Seite 1145-1160 Online-Ressource (DE-627)306713470 (DE-600)1501061-2 (DE-576)094058377 0377-2217 nnns volume:309 pages:1145-1160 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 Methoden und Techniken der Betriebswirtschaft VZ AR 309 1145-1160
allfieldsGer	10.1016/j.ejor.2023.02.031 doi (DE-627)ELV063005166 (ELSEVIER)S0377-2217(23)00164-9 DE-627 ger DE-627 rda eng 650 VZ 85.03 bkl Yang, Yu verfasserin (orcid)0000-0002-0502-7603 aut Planning robust drone-truck delivery routes under road traffic uncertainty 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we show the potential of a drone-truck logistics system to provide fast last-mile delivery services. In the system, a truck and a drone work in tandem to serve customers within pre-specified delivery time windows. Since the uncertainty in the ground traffic network can not only fail a service promise but also expose the drone to danger, we focus on mitigating such risks when designing the routing plan. In particular, we investigate the robust drone-truck delivery problem (RDTDP) that seeks a robust joint route for the truck-and-drone tandem to maximize the profit. We develop an exact branch-and-price (B&P) solution approach that can solve RDTDP instances, both randomly generated and collected from real-life data, with up to 40 service requests. In a numerical study, we demonstrate that the solution obtained with our proposed B&P approach is significantly more robust than the one obtained without considering any uncertainty. In particular, while maintaining a comparable mean value in the solution quality measures, the robust solution features a variance up to 58% smaller and a feasibility ratio (i.e., on-time performance) up to 90% higher than the deterministic solution. These insights suggest that the robust route can be carried out much more frequently in practical usage. Transportation Last-mile delivery Drone Robust optimization Branch-and-price Yan, Chiwei verfasserin aut Cao, Yufeng verfasserin (orcid)0000-0002-9183-0215 aut Roberti, Roberto verfasserin (orcid)0000-0002-2987-1593 aut Enthalten in European journal of operational research Amsterdam [u.a.] : Elsevier, 1977 309, Seite 1145-1160 Online-Ressource (DE-627)306713470 (DE-600)1501061-2 (DE-576)094058377 0377-2217 nnns volume:309 pages:1145-1160 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 Methoden und Techniken der Betriebswirtschaft VZ AR 309 1145-1160
allfieldsSound	10.1016/j.ejor.2023.02.031 doi (DE-627)ELV063005166 (ELSEVIER)S0377-2217(23)00164-9 DE-627 ger DE-627 rda eng 650 VZ 85.03 bkl Yang, Yu verfasserin (orcid)0000-0002-0502-7603 aut Planning robust drone-truck delivery routes under road traffic uncertainty 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we show the potential of a drone-truck logistics system to provide fast last-mile delivery services. In the system, a truck and a drone work in tandem to serve customers within pre-specified delivery time windows. Since the uncertainty in the ground traffic network can not only fail a service promise but also expose the drone to danger, we focus on mitigating such risks when designing the routing plan. In particular, we investigate the robust drone-truck delivery problem (RDTDP) that seeks a robust joint route for the truck-and-drone tandem to maximize the profit. We develop an exact branch-and-price (B&P) solution approach that can solve RDTDP instances, both randomly generated and collected from real-life data, with up to 40 service requests. In a numerical study, we demonstrate that the solution obtained with our proposed B&P approach is significantly more robust than the one obtained without considering any uncertainty. In particular, while maintaining a comparable mean value in the solution quality measures, the robust solution features a variance up to 58% smaller and a feasibility ratio (i.e., on-time performance) up to 90% higher than the deterministic solution. These insights suggest that the robust route can be carried out much more frequently in practical usage. Transportation Last-mile delivery Drone Robust optimization Branch-and-price Yan, Chiwei verfasserin aut Cao, Yufeng verfasserin (orcid)0000-0002-9183-0215 aut Roberti, Roberto verfasserin (orcid)0000-0002-2987-1593 aut Enthalten in European journal of operational research Amsterdam [u.a.] : Elsevier, 1977 309, Seite 1145-1160 Online-Ressource (DE-627)306713470 (DE-600)1501061-2 (DE-576)094058377 0377-2217 nnns volume:309 pages:1145-1160 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 Methoden und Techniken der Betriebswirtschaft VZ AR 309 1145-1160
language	English
source	Enthalten in European journal of operational research 309, Seite 1145-1160 volume:309 pages:1145-1160
sourceStr	Enthalten in European journal of operational research 309, Seite 1145-1160 volume:309 pages:1145-1160
format_phy_str_mv	Article
bklname	Methoden und Techniken der Betriebswirtschaft
institution	findex.gbv.de
topic_facet	Transportation Last-mile delivery Drone Robust optimization Branch-and-price
dewey-raw	650
isfreeaccess_bool	false
container_title	European journal of operational research
authorswithroles_txt_mv	Yang, Yu @@aut@@ Yan, Chiwei @@aut@@ Cao, Yufeng @@aut@@ Roberti, Roberto @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	306713470
dewey-sort	3650
id	ELV063005166
language_de	englisch
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author	Yang, Yu
spellingShingle	Yang, Yu ddc 650 bkl 85.03 misc Transportation misc Last-mile delivery misc Drone misc Robust optimization misc Branch-and-price Planning robust drone-truck delivery routes under road traffic uncertainty
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topic_title	650 VZ 85.03 bkl Planning robust drone-truck delivery routes under road traffic uncertainty Transportation Last-mile delivery Drone Robust optimization Branch-and-price
topic	ddc 650 bkl 85.03 misc Transportation misc Last-mile delivery misc Drone misc Robust optimization misc Branch-and-price
topic_unstemmed	ddc 650 bkl 85.03 misc Transportation misc Last-mile delivery misc Drone misc Robust optimization misc Branch-and-price
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title	Planning robust drone-truck delivery routes under road traffic uncertainty
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title_full	Planning robust drone-truck delivery routes under road traffic uncertainty
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title_sort	planning robust drone-truck delivery routes under road traffic uncertainty
title_auth	Planning robust drone-truck delivery routes under road traffic uncertainty
abstract	In this paper, we show the potential of a drone-truck logistics system to provide fast last-mile delivery services. In the system, a truck and a drone work in tandem to serve customers within pre-specified delivery time windows. Since the uncertainty in the ground traffic network can not only fail a service promise but also expose the drone to danger, we focus on mitigating such risks when designing the routing plan. In particular, we investigate the robust drone-truck delivery problem (RDTDP) that seeks a robust joint route for the truck-and-drone tandem to maximize the profit. We develop an exact branch-and-price (B&P) solution approach that can solve RDTDP instances, both randomly generated and collected from real-life data, with up to 40 service requests. In a numerical study, we demonstrate that the solution obtained with our proposed B&P approach is significantly more robust than the one obtained without considering any uncertainty. In particular, while maintaining a comparable mean value in the solution quality measures, the robust solution features a variance up to 58% smaller and a feasibility ratio (i.e., on-time performance) up to 90% higher than the deterministic solution. These insights suggest that the robust route can be carried out much more frequently in practical usage.
abstractGer	In this paper, we show the potential of a drone-truck logistics system to provide fast last-mile delivery services. In the system, a truck and a drone work in tandem to serve customers within pre-specified delivery time windows. Since the uncertainty in the ground traffic network can not only fail a service promise but also expose the drone to danger, we focus on mitigating such risks when designing the routing plan. In particular, we investigate the robust drone-truck delivery problem (RDTDP) that seeks a robust joint route for the truck-and-drone tandem to maximize the profit. We develop an exact branch-and-price (B&P) solution approach that can solve RDTDP instances, both randomly generated and collected from real-life data, with up to 40 service requests. In a numerical study, we demonstrate that the solution obtained with our proposed B&P approach is significantly more robust than the one obtained without considering any uncertainty. In particular, while maintaining a comparable mean value in the solution quality measures, the robust solution features a variance up to 58% smaller and a feasibility ratio (i.e., on-time performance) up to 90% higher than the deterministic solution. These insights suggest that the robust route can be carried out much more frequently in practical usage.
abstract_unstemmed	In this paper, we show the potential of a drone-truck logistics system to provide fast last-mile delivery services. In the system, a truck and a drone work in tandem to serve customers within pre-specified delivery time windows. Since the uncertainty in the ground traffic network can not only fail a service promise but also expose the drone to danger, we focus on mitigating such risks when designing the routing plan. In particular, we investigate the robust drone-truck delivery problem (RDTDP) that seeks a robust joint route for the truck-and-drone tandem to maximize the profit. We develop an exact branch-and-price (B&P) solution approach that can solve RDTDP instances, both randomly generated and collected from real-life data, with up to 40 service requests. In a numerical study, we demonstrate that the solution obtained with our proposed B&P approach is significantly more robust than the one obtained without considering any uncertainty. In particular, while maintaining a comparable mean value in the solution quality measures, the robust solution features a variance up to 58% smaller and a feasibility ratio (i.e., on-time performance) up to 90% higher than the deterministic solution. These insights suggest that the robust route can be carried out much more frequently in practical usage.
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title_short	Planning robust drone-truck delivery routes under road traffic uncertainty
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author2	Yan, Chiwei Cao, Yufeng Roberti, Roberto
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doi_str	10.1016/j.ejor.2023.02.031
up_date	2024-07-06T19:19:59.378Z
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