Multi-objective mammography unit location-allocation problem : a case study

Gespeichert in:

Autor*in:	Campos, Marcos Vinícius Andrade de [verfasserIn] Assis, Romário dos Santos Lopes de [verfasserIn] Souza, Marcone Jamilson Freitas [verfasserIn] Siqueira, Eduardo Camargo de [verfasserIn] Silva, Maria Amélia Lopes [verfasserIn] Souza, Sérgio Ricardo de [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Facility location Location-allocation problem Mammography screening Multi-objective optimization NSGA-II SPEA2

Übergeordnetes Werk:	Enthalten in: Operations research for health care - Amsterdam [u.a.] : Elsevier, 2012, 41(2024) vom: Juni, Artikel-ID 100430, Seite 1-16
Übergeordnetes Werk:	volume:41 ; year:2024 ; month:06 ; elocationid:100430 ; pages:1-16

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1016/j.orhc.2024.100430

Katalog-ID:	1902816811

Internformat


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Indexfelder

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allfields	10.1016/j.orhc.2024.100430 doi (DE-627)1902816811 (DE-599)KXP1902816811 DE-627 ger DE-627 rda eng Campos, Marcos Vinícius Andrade de verfasserin (DE-588)1342336496 (DE-627)1902824776 aut Multi-objective mammography unit location-allocation problem a case study Marcos Vinícius Andrade de Campos, Romário dos Santos Lopes de Assis, Marcone Jamilson Freitas Souza, Eduardo Camargo de Siqueira, Maria Amélia Lopes Silva, Sérgio Ricardo de Souza 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Facility location (dpeaa)DE-206 Location-allocation problem (dpeaa)DE-206 Mammography screening (dpeaa)DE-206 Multi-objective optimization (dpeaa)DE-206 NSGA-II (dpeaa)DE-206 SPEA2 (dpeaa)DE-206 Assis, Romário dos Santos Lopes de verfasserin (DE-588)1342337050 (DE-627)1902825446 aut Souza, Marcone Jamilson Freitas verfasserin (DE-588)1133514456 (DE-627)889296421 (DE-576)489459528 aut Siqueira, Eduardo Camargo de verfasserin (DE-588)1342338103 (DE-627)1902826752 aut Silva, Maria Amélia Lopes verfasserin (DE-588)1342338359 (DE-627)1902827201 aut Souza, Sérgio Ricardo de verfasserin (DE-588)1133514715 (DE-627)889296820 (DE-576)489459943 aut Enthalten in Operations research for health care Amsterdam [u.a.] : Elsevier, 2012 41(2024) vom: Juni, Artikel-ID 100430, Seite 1-16 Online-Ressource (DE-627)688080626 (DE-600)2653283-9 (DE-576)361843348 2211-6931 nnns volume:41 year:2024 month:06 elocationid:100430 pages:1-16 https://www.sciencedirect.com/science/article/pii/S2211692324000110/pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.orhc.2024.100430 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 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_2034 GBV_ILN_2038 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_2068 GBV_ILN_2088 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_2522 GBV_ILN_4035 GBV_ILN_4037 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2024 6 100430 1-16 26 01 0206 4581242878 x1z 18-09-24 26 00 DE-206 This work addresses the Multi-Objective Mammography Unit Location–allocation Problem (MOMULAP), aiming to meet three objectives: maximize mammography screening coverage, minimize the total distance traveled weighted by the number of users, and maximize equity in access to mammography screening. We introduce a mixed-integer nonlinear programming (MINLP) formulation to represent the MOMULAP and algorithms based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA2) for treating it. The algorithms were tested with data from seven Brazilian states. In these states, the number of cities ranges from 139 to 853, equipment from 23 to 347 units, and estimated annual demand for screenings from 96,592 to 1,739,085. The solutions provided by this work allow health managers to choose the appropriate location and allocation of the mammography units, considering different objectives.
spelling	10.1016/j.orhc.2024.100430 doi (DE-627)1902816811 (DE-599)KXP1902816811 DE-627 ger DE-627 rda eng Campos, Marcos Vinícius Andrade de verfasserin (DE-588)1342336496 (DE-627)1902824776 aut Multi-objective mammography unit location-allocation problem a case study Marcos Vinícius Andrade de Campos, Romário dos Santos Lopes de Assis, Marcone Jamilson Freitas Souza, Eduardo Camargo de Siqueira, Maria Amélia Lopes Silva, Sérgio Ricardo de Souza 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Facility location (dpeaa)DE-206 Location-allocation problem (dpeaa)DE-206 Mammography screening (dpeaa)DE-206 Multi-objective optimization (dpeaa)DE-206 NSGA-II (dpeaa)DE-206 SPEA2 (dpeaa)DE-206 Assis, Romário dos Santos Lopes de verfasserin (DE-588)1342337050 (DE-627)1902825446 aut Souza, Marcone Jamilson Freitas verfasserin (DE-588)1133514456 (DE-627)889296421 (DE-576)489459528 aut Siqueira, Eduardo Camargo de verfasserin (DE-588)1342338103 (DE-627)1902826752 aut Silva, Maria Amélia Lopes verfasserin (DE-588)1342338359 (DE-627)1902827201 aut Souza, Sérgio Ricardo de verfasserin (DE-588)1133514715 (DE-627)889296820 (DE-576)489459943 aut Enthalten in Operations research for health care Amsterdam [u.a.] : Elsevier, 2012 41(2024) vom: Juni, Artikel-ID 100430, Seite 1-16 Online-Ressource (DE-627)688080626 (DE-600)2653283-9 (DE-576)361843348 2211-6931 nnns volume:41 year:2024 month:06 elocationid:100430 pages:1-16 https://www.sciencedirect.com/science/article/pii/S2211692324000110/pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.orhc.2024.100430 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 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_2034 GBV_ILN_2038 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_2068 GBV_ILN_2088 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_2522 GBV_ILN_4035 GBV_ILN_4037 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2024 6 100430 1-16 26 01 0206 4581242878 x1z 18-09-24 26 00 DE-206 This work addresses the Multi-Objective Mammography Unit Location–allocation Problem (MOMULAP), aiming to meet three objectives: maximize mammography screening coverage, minimize the total distance traveled weighted by the number of users, and maximize equity in access to mammography screening. We introduce a mixed-integer nonlinear programming (MINLP) formulation to represent the MOMULAP and algorithms based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA2) for treating it. The algorithms were tested with data from seven Brazilian states. In these states, the number of cities ranges from 139 to 853, equipment from 23 to 347 units, and estimated annual demand for screenings from 96,592 to 1,739,085. The solutions provided by this work allow health managers to choose the appropriate location and allocation of the mammography units, considering different objectives.
allfields_unstemmed	10.1016/j.orhc.2024.100430 doi (DE-627)1902816811 (DE-599)KXP1902816811 DE-627 ger DE-627 rda eng Campos, Marcos Vinícius Andrade de verfasserin (DE-588)1342336496 (DE-627)1902824776 aut Multi-objective mammography unit location-allocation problem a case study Marcos Vinícius Andrade de Campos, Romário dos Santos Lopes de Assis, Marcone Jamilson Freitas Souza, Eduardo Camargo de Siqueira, Maria Amélia Lopes Silva, Sérgio Ricardo de Souza 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Facility location (dpeaa)DE-206 Location-allocation problem (dpeaa)DE-206 Mammography screening (dpeaa)DE-206 Multi-objective optimization (dpeaa)DE-206 NSGA-II (dpeaa)DE-206 SPEA2 (dpeaa)DE-206 Assis, Romário dos Santos Lopes de verfasserin (DE-588)1342337050 (DE-627)1902825446 aut Souza, Marcone Jamilson Freitas verfasserin (DE-588)1133514456 (DE-627)889296421 (DE-576)489459528 aut Siqueira, Eduardo Camargo de verfasserin (DE-588)1342338103 (DE-627)1902826752 aut Silva, Maria Amélia Lopes verfasserin (DE-588)1342338359 (DE-627)1902827201 aut Souza, Sérgio Ricardo de verfasserin (DE-588)1133514715 (DE-627)889296820 (DE-576)489459943 aut Enthalten in Operations research for health care Amsterdam [u.a.] : Elsevier, 2012 41(2024) vom: Juni, Artikel-ID 100430, Seite 1-16 Online-Ressource (DE-627)688080626 (DE-600)2653283-9 (DE-576)361843348 2211-6931 nnns volume:41 year:2024 month:06 elocationid:100430 pages:1-16 https://www.sciencedirect.com/science/article/pii/S2211692324000110/pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.orhc.2024.100430 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 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_2034 GBV_ILN_2038 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_2068 GBV_ILN_2088 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_2522 GBV_ILN_4035 GBV_ILN_4037 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2024 6 100430 1-16 26 01 0206 4581242878 x1z 18-09-24 26 00 DE-206 This work addresses the Multi-Objective Mammography Unit Location–allocation Problem (MOMULAP), aiming to meet three objectives: maximize mammography screening coverage, minimize the total distance traveled weighted by the number of users, and maximize equity in access to mammography screening. We introduce a mixed-integer nonlinear programming (MINLP) formulation to represent the MOMULAP and algorithms based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA2) for treating it. The algorithms were tested with data from seven Brazilian states. In these states, the number of cities ranges from 139 to 853, equipment from 23 to 347 units, and estimated annual demand for screenings from 96,592 to 1,739,085. The solutions provided by this work allow health managers to choose the appropriate location and allocation of the mammography units, considering different objectives.
allfieldsGer	10.1016/j.orhc.2024.100430 doi (DE-627)1902816811 (DE-599)KXP1902816811 DE-627 ger DE-627 rda eng Campos, Marcos Vinícius Andrade de verfasserin (DE-588)1342336496 (DE-627)1902824776 aut Multi-objective mammography unit location-allocation problem a case study Marcos Vinícius Andrade de Campos, Romário dos Santos Lopes de Assis, Marcone Jamilson Freitas Souza, Eduardo Camargo de Siqueira, Maria Amélia Lopes Silva, Sérgio Ricardo de Souza 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Facility location (dpeaa)DE-206 Location-allocation problem (dpeaa)DE-206 Mammography screening (dpeaa)DE-206 Multi-objective optimization (dpeaa)DE-206 NSGA-II (dpeaa)DE-206 SPEA2 (dpeaa)DE-206 Assis, Romário dos Santos Lopes de verfasserin (DE-588)1342337050 (DE-627)1902825446 aut Souza, Marcone Jamilson Freitas verfasserin (DE-588)1133514456 (DE-627)889296421 (DE-576)489459528 aut Siqueira, Eduardo Camargo de verfasserin (DE-588)1342338103 (DE-627)1902826752 aut Silva, Maria Amélia Lopes verfasserin (DE-588)1342338359 (DE-627)1902827201 aut Souza, Sérgio Ricardo de verfasserin (DE-588)1133514715 (DE-627)889296820 (DE-576)489459943 aut Enthalten in Operations research for health care Amsterdam [u.a.] : Elsevier, 2012 41(2024) vom: Juni, Artikel-ID 100430, Seite 1-16 Online-Ressource (DE-627)688080626 (DE-600)2653283-9 (DE-576)361843348 2211-6931 nnns volume:41 year:2024 month:06 elocationid:100430 pages:1-16 https://www.sciencedirect.com/science/article/pii/S2211692324000110/pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.orhc.2024.100430 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 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_2034 GBV_ILN_2038 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_2068 GBV_ILN_2088 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_2522 GBV_ILN_4035 GBV_ILN_4037 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2024 6 100430 1-16 26 01 0206 4581242878 x1z 18-09-24 26 00 DE-206 This work addresses the Multi-Objective Mammography Unit Location–allocation Problem (MOMULAP), aiming to meet three objectives: maximize mammography screening coverage, minimize the total distance traveled weighted by the number of users, and maximize equity in access to mammography screening. We introduce a mixed-integer nonlinear programming (MINLP) formulation to represent the MOMULAP and algorithms based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA2) for treating it. The algorithms were tested with data from seven Brazilian states. In these states, the number of cities ranges from 139 to 853, equipment from 23 to 347 units, and estimated annual demand for screenings from 96,592 to 1,739,085. The solutions provided by this work allow health managers to choose the appropriate location and allocation of the mammography units, considering different objectives.
allfieldsSound	10.1016/j.orhc.2024.100430 doi (DE-627)1902816811 (DE-599)KXP1902816811 DE-627 ger DE-627 rda eng Campos, Marcos Vinícius Andrade de verfasserin (DE-588)1342336496 (DE-627)1902824776 aut Multi-objective mammography unit location-allocation problem a case study Marcos Vinícius Andrade de Campos, Romário dos Santos Lopes de Assis, Marcone Jamilson Freitas Souza, Eduardo Camargo de Siqueira, Maria Amélia Lopes Silva, Sérgio Ricardo de Souza 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Facility location (dpeaa)DE-206 Location-allocation problem (dpeaa)DE-206 Mammography screening (dpeaa)DE-206 Multi-objective optimization (dpeaa)DE-206 NSGA-II (dpeaa)DE-206 SPEA2 (dpeaa)DE-206 Assis, Romário dos Santos Lopes de verfasserin (DE-588)1342337050 (DE-627)1902825446 aut Souza, Marcone Jamilson Freitas verfasserin (DE-588)1133514456 (DE-627)889296421 (DE-576)489459528 aut Siqueira, Eduardo Camargo de verfasserin (DE-588)1342338103 (DE-627)1902826752 aut Silva, Maria Amélia Lopes verfasserin (DE-588)1342338359 (DE-627)1902827201 aut Souza, Sérgio Ricardo de verfasserin (DE-588)1133514715 (DE-627)889296820 (DE-576)489459943 aut Enthalten in Operations research for health care Amsterdam [u.a.] : Elsevier, 2012 41(2024) vom: Juni, Artikel-ID 100430, Seite 1-16 Online-Ressource (DE-627)688080626 (DE-600)2653283-9 (DE-576)361843348 2211-6931 nnns volume:41 year:2024 month:06 elocationid:100430 pages:1-16 https://www.sciencedirect.com/science/article/pii/S2211692324000110/pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.orhc.2024.100430 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 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_2034 GBV_ILN_2038 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_2068 GBV_ILN_2088 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_2522 GBV_ILN_4035 GBV_ILN_4037 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2024 6 100430 1-16 26 01 0206 4581242878 x1z 18-09-24 26 00 DE-206 This work addresses the Multi-Objective Mammography Unit Location–allocation Problem (MOMULAP), aiming to meet three objectives: maximize mammography screening coverage, minimize the total distance traveled weighted by the number of users, and maximize equity in access to mammography screening. We introduce a mixed-integer nonlinear programming (MINLP) formulation to represent the MOMULAP and algorithms based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA2) for treating it. The algorithms were tested with data from seven Brazilian states. In these states, the number of cities ranges from 139 to 853, equipment from 23 to 347 units, and estimated annual demand for screenings from 96,592 to 1,739,085. The solutions provided by this work allow health managers to choose the appropriate location and allocation of the mammography units, considering different objectives.
language	English
source	Enthalten in Operations research for health care 41(2024) vom: Juni, Artikel-ID 100430, Seite 1-16 volume:41 year:2024 month:06 elocationid:100430 pages:1-16
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topic_facet	Facility location Location-allocation problem Mammography screening Multi-objective optimization NSGA-II SPEA2
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container_title	Operations research for health care
authorswithroles_txt_mv	Campos, Marcos Vinícius Andrade de @@aut@@ Assis, Romário dos Santos Lopes de @@aut@@ Souza, Marcone Jamilson Freitas @@aut@@ Siqueira, Eduardo Camargo de @@aut@@ Silva, Maria Amélia Lopes @@aut@@ Souza, Sérgio Ricardo de @@aut@@
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author	Campos, Marcos Vinícius Andrade de
spellingShingle	Campos, Marcos Vinícius Andrade de misc Facility location misc Location-allocation problem misc Mammography screening misc Multi-objective optimization misc NSGA-II misc SPEA2 Multi-objective mammography unit location-allocation problem a case study
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topic_title	26 00 DE-206 This work addresses the Multi-Objective Mammography Unit Location–allocation Problem (MOMULAP), aiming to meet three objectives: maximize mammography screening coverage, minimize the total distance traveled weighted by the number of users, and maximize equity in access to mammography screening. We introduce a mixed-integer nonlinear programming (MINLP) formulation to represent the MOMULAP and algorithms based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA2) for treating it. The algorithms were tested with data from seven Brazilian states. In these states, the number of cities ranges from 139 to 853, equipment from 23 to 347 units, and estimated annual demand for screenings from 96,592 to 1,739,085. The solutions provided by this work allow health managers to choose the appropriate location and allocation of the mammography units, considering different objectives Multi-objective mammography unit location-allocation problem a case study Marcos Vinícius Andrade de Campos, Romário dos Santos Lopes de Assis, Marcone Jamilson Freitas Souza, Eduardo Camargo de Siqueira, Maria Amélia Lopes Silva, Sérgio Ricardo de Souza Facility location (dpeaa)DE-206 Location-allocation problem (dpeaa)DE-206 Mammography screening (dpeaa)DE-206 Multi-objective optimization (dpeaa)DE-206 NSGA-II (dpeaa)DE-206 SPEA2 (dpeaa)DE-206
topic	misc Facility location misc Location-allocation problem misc Mammography screening misc Multi-objective optimization misc NSGA-II misc SPEA2
topic_unstemmed	misc Facility location misc Location-allocation problem misc Mammography screening misc Multi-objective optimization misc NSGA-II misc SPEA2
topic_browse	misc Facility location misc Location-allocation problem misc Mammography screening misc Multi-objective optimization misc NSGA-II misc SPEA2
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title	Multi-objective mammography unit location-allocation problem a case study
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title_full	Multi-objective mammography unit location-allocation problem a case study Marcos Vinícius Andrade de Campos, Romário dos Santos Lopes de Assis, Marcone Jamilson Freitas Souza, Eduardo Camargo de Siqueira, Maria Amélia Lopes Silva, Sérgio Ricardo de Souza
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title_sort	multi-objective mammography unit location-allocation problema case study
title_auth	Multi-objective mammography unit location-allocation problem a case study
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title_short	Multi-objective mammography unit location-allocation problem
url	https://www.sciencedirect.com/science/article/pii/S2211692324000110/pdf https://doi.org/10.1016/j.orhc.2024.100430
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up_date	2024-09-19T07:55:20.833Z
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