Acceleration strategies for the weight constrained shortest path problem with replenishment

Abstract The weight constrained shortest path problem with replenishment (WCSPP-R) generalizes the constrained shortest path problem (CSP) and has multiple applications in transportation, scheduling, and telecommunications. We present an exact algorithm based on a recursive depth-first search that c...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Bolívar, Manuel A. [verfasserIn] Lozano, Leonardo Medaglia, Andrés L.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Constrained shortest path problem replenishment large-scale networks

Anmerkung:	© Springer-Verlag Berlin Heidelberg 2014

Übergeordnetes Werk:	Enthalten in: Optimization letters - Berlin : Springer, 2007, 8(2014), 8 vom: 17. Apr., Seite 2155-2172
Übergeordnetes Werk:	volume:8 ; year:2014 ; number:8 ; day:17 ; month:04 ; pages:2155-2172

Links:	Volltext

DOI / URN:	10.1007/s11590-014-0742-x

Katalog-ID:	SPR020961480

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520			\|a Abstract The weight constrained shortest path problem with replenishment (WCSPP-R) generalizes the constrained shortest path problem (CSP) and has multiple applications in transportation, scheduling, and telecommunications. We present an exact algorithm based on a recursive depth-first search that combines and extends ideas proposed in state-of-the-art algorithms for the CSP and the WCSPP-R. The novelty lies in a set of acceleration strategies that significantly improves the algorithm’s performance. We conducted experiments over large real-road networks with up to 6 million nodes and 15 million arcs, achieving speedups of up to 219 times against the state-of-the-art algorithm.
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700	1		\|a Lozano, Leonardo \|4 aut
700	1		\|a Medaglia, Andrés L. \|4 aut
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allfields	10.1007/s11590-014-0742-x doi (DE-627)SPR020961480 (SPR)s11590-014-0742-x-e DE-627 ger DE-627 rakwb eng Bolívar, Manuel A. verfasserin aut Acceleration strategies for the weight constrained shortest path problem with replenishment 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract The weight constrained shortest path problem with replenishment (WCSPP-R) generalizes the constrained shortest path problem (CSP) and has multiple applications in transportation, scheduling, and telecommunications. We present an exact algorithm based on a recursive depth-first search that combines and extends ideas proposed in state-of-the-art algorithms for the CSP and the WCSPP-R. The novelty lies in a set of acceleration strategies that significantly improves the algorithm’s performance. We conducted experiments over large real-road networks with up to 6 million nodes and 15 million arcs, achieving speedups of up to 219 times against the state-of-the-art algorithm. Constrained shortest path problem (dpeaa)DE-He213 replenishment (dpeaa)DE-He213 large-scale networks (dpeaa)DE-He213 Lozano, Leonardo aut Medaglia, Andrés L. aut Enthalten in Optimization letters Berlin : Springer, 2007 8(2014), 8 vom: 17. Apr., Seite 2155-2172 (DE-627)534676499 (DE-600)2374345-1 1862-4480 nnns volume:8 year:2014 number:8 day:17 month:04 pages:2155-2172 https://dx.doi.org/10.1007/s11590-014-0742-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2014 8 17 04 2155-2172
spelling	10.1007/s11590-014-0742-x doi (DE-627)SPR020961480 (SPR)s11590-014-0742-x-e DE-627 ger DE-627 rakwb eng Bolívar, Manuel A. verfasserin aut Acceleration strategies for the weight constrained shortest path problem with replenishment 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract The weight constrained shortest path problem with replenishment (WCSPP-R) generalizes the constrained shortest path problem (CSP) and has multiple applications in transportation, scheduling, and telecommunications. We present an exact algorithm based on a recursive depth-first search that combines and extends ideas proposed in state-of-the-art algorithms for the CSP and the WCSPP-R. The novelty lies in a set of acceleration strategies that significantly improves the algorithm’s performance. We conducted experiments over large real-road networks with up to 6 million nodes and 15 million arcs, achieving speedups of up to 219 times against the state-of-the-art algorithm. Constrained shortest path problem (dpeaa)DE-He213 replenishment (dpeaa)DE-He213 large-scale networks (dpeaa)DE-He213 Lozano, Leonardo aut Medaglia, Andrés L. aut Enthalten in Optimization letters Berlin : Springer, 2007 8(2014), 8 vom: 17. Apr., Seite 2155-2172 (DE-627)534676499 (DE-600)2374345-1 1862-4480 nnns volume:8 year:2014 number:8 day:17 month:04 pages:2155-2172 https://dx.doi.org/10.1007/s11590-014-0742-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2014 8 17 04 2155-2172
allfields_unstemmed	10.1007/s11590-014-0742-x doi (DE-627)SPR020961480 (SPR)s11590-014-0742-x-e DE-627 ger DE-627 rakwb eng Bolívar, Manuel A. verfasserin aut Acceleration strategies for the weight constrained shortest path problem with replenishment 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract The weight constrained shortest path problem with replenishment (WCSPP-R) generalizes the constrained shortest path problem (CSP) and has multiple applications in transportation, scheduling, and telecommunications. We present an exact algorithm based on a recursive depth-first search that combines and extends ideas proposed in state-of-the-art algorithms for the CSP and the WCSPP-R. The novelty lies in a set of acceleration strategies that significantly improves the algorithm’s performance. We conducted experiments over large real-road networks with up to 6 million nodes and 15 million arcs, achieving speedups of up to 219 times against the state-of-the-art algorithm. Constrained shortest path problem (dpeaa)DE-He213 replenishment (dpeaa)DE-He213 large-scale networks (dpeaa)DE-He213 Lozano, Leonardo aut Medaglia, Andrés L. aut Enthalten in Optimization letters Berlin : Springer, 2007 8(2014), 8 vom: 17. Apr., Seite 2155-2172 (DE-627)534676499 (DE-600)2374345-1 1862-4480 nnns volume:8 year:2014 number:8 day:17 month:04 pages:2155-2172 https://dx.doi.org/10.1007/s11590-014-0742-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2014 8 17 04 2155-2172
allfieldsGer	10.1007/s11590-014-0742-x doi (DE-627)SPR020961480 (SPR)s11590-014-0742-x-e DE-627 ger DE-627 rakwb eng Bolívar, Manuel A. verfasserin aut Acceleration strategies for the weight constrained shortest path problem with replenishment 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract The weight constrained shortest path problem with replenishment (WCSPP-R) generalizes the constrained shortest path problem (CSP) and has multiple applications in transportation, scheduling, and telecommunications. We present an exact algorithm based on a recursive depth-first search that combines and extends ideas proposed in state-of-the-art algorithms for the CSP and the WCSPP-R. The novelty lies in a set of acceleration strategies that significantly improves the algorithm’s performance. We conducted experiments over large real-road networks with up to 6 million nodes and 15 million arcs, achieving speedups of up to 219 times against the state-of-the-art algorithm. Constrained shortest path problem (dpeaa)DE-He213 replenishment (dpeaa)DE-He213 large-scale networks (dpeaa)DE-He213 Lozano, Leonardo aut Medaglia, Andrés L. aut Enthalten in Optimization letters Berlin : Springer, 2007 8(2014), 8 vom: 17. Apr., Seite 2155-2172 (DE-627)534676499 (DE-600)2374345-1 1862-4480 nnns volume:8 year:2014 number:8 day:17 month:04 pages:2155-2172 https://dx.doi.org/10.1007/s11590-014-0742-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2014 8 17 04 2155-2172
allfieldsSound	10.1007/s11590-014-0742-x doi (DE-627)SPR020961480 (SPR)s11590-014-0742-x-e DE-627 ger DE-627 rakwb eng Bolívar, Manuel A. verfasserin aut Acceleration strategies for the weight constrained shortest path problem with replenishment 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2014 Abstract The weight constrained shortest path problem with replenishment (WCSPP-R) generalizes the constrained shortest path problem (CSP) and has multiple applications in transportation, scheduling, and telecommunications. We present an exact algorithm based on a recursive depth-first search that combines and extends ideas proposed in state-of-the-art algorithms for the CSP and the WCSPP-R. The novelty lies in a set of acceleration strategies that significantly improves the algorithm’s performance. We conducted experiments over large real-road networks with up to 6 million nodes and 15 million arcs, achieving speedups of up to 219 times against the state-of-the-art algorithm. Constrained shortest path problem (dpeaa)DE-He213 replenishment (dpeaa)DE-He213 large-scale networks (dpeaa)DE-He213 Lozano, Leonardo aut Medaglia, Andrés L. aut Enthalten in Optimization letters Berlin : Springer, 2007 8(2014), 8 vom: 17. Apr., Seite 2155-2172 (DE-627)534676499 (DE-600)2374345-1 1862-4480 nnns volume:8 year:2014 number:8 day:17 month:04 pages:2155-2172 https://dx.doi.org/10.1007/s11590-014-0742-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2014 8 17 04 2155-2172
language	English
source	Enthalten in Optimization letters 8(2014), 8 vom: 17. Apr., Seite 2155-2172 volume:8 year:2014 number:8 day:17 month:04 pages:2155-2172
sourceStr	Enthalten in Optimization letters 8(2014), 8 vom: 17. Apr., Seite 2155-2172 volume:8 year:2014 number:8 day:17 month:04 pages:2155-2172
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Constrained shortest path problem replenishment large-scale networks
isfreeaccess_bool	false
container_title	Optimization letters
authorswithroles_txt_mv	Bolívar, Manuel A. @@aut@@ Lozano, Leonardo @@aut@@ Medaglia, Andrés L. @@aut@@
publishDateDaySort_date	2014-04-17T00:00:00Z
hierarchy_top_id	534676499
id	SPR020961480
language_de	englisch
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author	Bolívar, Manuel A.
spellingShingle	Bolívar, Manuel A. misc Constrained shortest path problem misc replenishment misc large-scale networks Acceleration strategies for the weight constrained shortest path problem with replenishment
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topic_title	Acceleration strategies for the weight constrained shortest path problem with replenishment Constrained shortest path problem (dpeaa)DE-He213 replenishment (dpeaa)DE-He213 large-scale networks (dpeaa)DE-He213
topic	misc Constrained shortest path problem misc replenishment misc large-scale networks
topic_unstemmed	misc Constrained shortest path problem misc replenishment misc large-scale networks
topic_browse	misc Constrained shortest path problem misc replenishment misc large-scale networks
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title	Acceleration strategies for the weight constrained shortest path problem with replenishment
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title_full	Acceleration strategies for the weight constrained shortest path problem with replenishment
author_sort	Bolívar, Manuel A.
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author_browse	Bolívar, Manuel A. Lozano, Leonardo Medaglia, Andrés L.
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author-letter	Bolívar, Manuel A.
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title_sort	acceleration strategies for the weight constrained shortest path problem with replenishment
title_auth	Acceleration strategies for the weight constrained shortest path problem with replenishment
abstract	Abstract The weight constrained shortest path problem with replenishment (WCSPP-R) generalizes the constrained shortest path problem (CSP) and has multiple applications in transportation, scheduling, and telecommunications. We present an exact algorithm based on a recursive depth-first search that combines and extends ideas proposed in state-of-the-art algorithms for the CSP and the WCSPP-R. The novelty lies in a set of acceleration strategies that significantly improves the algorithm’s performance. We conducted experiments over large real-road networks with up to 6 million nodes and 15 million arcs, achieving speedups of up to 219 times against the state-of-the-art algorithm. © Springer-Verlag Berlin Heidelberg 2014
abstractGer	Abstract The weight constrained shortest path problem with replenishment (WCSPP-R) generalizes the constrained shortest path problem (CSP) and has multiple applications in transportation, scheduling, and telecommunications. We present an exact algorithm based on a recursive depth-first search that combines and extends ideas proposed in state-of-the-art algorithms for the CSP and the WCSPP-R. The novelty lies in a set of acceleration strategies that significantly improves the algorithm’s performance. We conducted experiments over large real-road networks with up to 6 million nodes and 15 million arcs, achieving speedups of up to 219 times against the state-of-the-art algorithm. © Springer-Verlag Berlin Heidelberg 2014
abstract_unstemmed	Abstract The weight constrained shortest path problem with replenishment (WCSPP-R) generalizes the constrained shortest path problem (CSP) and has multiple applications in transportation, scheduling, and telecommunications. We present an exact algorithm based on a recursive depth-first search that combines and extends ideas proposed in state-of-the-art algorithms for the CSP and the WCSPP-R. The novelty lies in a set of acceleration strategies that significantly improves the algorithm’s performance. We conducted experiments over large real-road networks with up to 6 million nodes and 15 million arcs, achieving speedups of up to 219 times against the state-of-the-art algorithm. © Springer-Verlag Berlin Heidelberg 2014
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title_short	Acceleration strategies for the weight constrained shortest path problem with replenishment
url	https://dx.doi.org/10.1007/s11590-014-0742-x
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author2	Lozano, Leonardo Medaglia, Andrés L.
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up_date	2024-07-03T19:22:45.668Z
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Acceleration strategies for the weight constrained shortest path problem with replenishment

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