- Bernardino, R., Paias, A. “Metaheuristics based on decision hierarchies for the traveling purchaser problem”. Intl. Trans. in Op. Res., 25 (4), 1269-1295, 2018 doi:10.1111/itor.12330
- Mesquita, M., Murta, A. G., Paias, A. and Wise, L., A metaheuristic approach to fisheries survey route planning. Intl. Trans. in Op. Res., 24 (3), pp 439-464, 2017. doi:10.1111/itor.12252
- Bautzer, A., Gouveia, L., Paias, A., Pires, J. “Models for a Steiner multi-ring network design problem with revenues”, TOP 24(2) 360-380, 2016. doi:10.1007/s11750-015-0388-6
- M. Mesquita, M. Moz, A. Paias, M. Pato, " A decompose-and-fix heuristic based on multi-commodity flow models for driver rostering with days-off pattern”, European Journal of Operations Research, vol. 245, issue 2 , pp. 423-437, 2015. http://dx.doi.org/10.1016/j.ejor.2015.03.030
- Bernardino, R. and Paias, Solving the family traveling salesman problem. , European Journal of Operations Research, vol. 267, issue 2, 2018. doi:10.1016/j.ejor.2017.11.063
Ana Maria Duarte Silva Alves Paias
Contactos
Departamento de Estatística e Investigação OperacionalSala/Gabinete 6.4.15
Ext. Principal 526415
Telefone Direto 217500408
Extensão pessoal 526415
Email ampaias@ciencias.ulisboa.pt
Carreira Docente Universitário
Categoria Professor Auxiliar
Indicadores
ResearcherIDOrcid
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Intl. Trans. in Op. Res., 25 (4), 1269-1295, 2018 doi:10.1111/itor.12330', 'pub2' => 'Mesquita, M., Murta, A. G., Paias, A. and Wise, L., A metaheuristic approach to fisheries survey route planning. Intl. Trans. in Op. Res., 24 (3), pp 439-464, 2017. doi:10.1111/itor.12252', 'pub3' => 'Bautzer, A., Gouveia, L., Paias, A., Pires, J. “Models for a Steiner multi-ring network design problem with revenues”, TOP 24(2) 360-380, 2016. doi:10.1007/s11750-015-0388-6', 'pub4' => 'M. Mesquita, M. Moz, A. Paias, M. Pato, " A decompose-and-fix heuristic based on multi-commodity flow models for driver rostering with days-off pattern”, European Journal of Operations Research, vol. 245, issue 2 , pp. 423-437, 2015. http://dx.doi.org/10.1016/j.ejor.2015.03.030', 'pub5' => 'Bernardino, R. and Paias, Solving the family traveling salesman problem. , European Journal of Operations Research, vol. 267, issue 2, 2018. doi:10.1016/j.ejor.2017.11.063', 'email_publico' => null, 'foto' => 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', 'nome_foto' => 'ana2.jpg' ) ) $visita = (int) 1 $utilizador = array( 'bi' => '06167658', 'nome_completo' => 'Ana Maria Duarte Silva Alves Paias', 'nome_a_mostrar' => '', 'username' => 'ampaias', 'mail' => 'ampaias@ciencias.ulisboa.pt', 'pagina_pessoal' => null, 'cv_resumido' => null, 'extpessoal' => '526415', 'interesses_cientificos' => 'Problemas de determinação de rotas<br>Escalonamento de viaturas e tripulações<br>Rotações<br>Modelos MILP<br>Heurísticas', 'scientific_interests' => 'Routing problems<br>Vehicle and crew scheduling<br>Rostering<br>MILP models<br>Heuristics', 'orcid' => '0000-0002-1339-1974', 'researcherID' => 'M-1764-2015', 'scopus' => '', 'google_scholar' => '', 'cienciaID' => '', 'keywords' => '', 'palavraschave' => '', 'carreira' => 'Docente Universitário', 'categoria' => 'Professor Auxiliar', 'unidade' => 'Departamento de EstatÃstica e Investigação Operacional', 'inativo' => false, 'contactos_sala' => '6.4.15', 'contactos_extensao_principal' => '526415', 'contactos_telefone_directo' => '217500408', 'pub1' => 'Bernardino, R., Paias, A. “Metaheuristics based on decision hierarchies for the traveling purchaser problem”. Intl. Trans. in Op. Res., 25 (4), 1269-1295, 2018 doi:10.1111/itor.12330', 'pub2' => 'Mesquita, M., Murta, A. G., Paias, A. and Wise, L., A metaheuristic approach to fisheries survey route planning. Intl. Trans. in Op. Res., 24 (3), pp 439-464, 2017. doi:10.1111/itor.12252', 'pub3' => 'Bautzer, A., Gouveia, L., Paias, A., Pires, J. “Models for a Steiner multi-ring network design problem with revenues”, TOP 24(2) 360-380, 2016. doi:10.1007/s11750-015-0388-6', 'pub4' => 'M. Mesquita, M. Moz, A. Paias, M. Pato, " A decompose-and-fix heuristic based on multi-commodity flow models for driver rostering with days-off pattern”, European Journal of Operations Research, vol. 245, issue 2 , pp. 423-437, 2015. http://dx.doi.org/10.1016/j.ejor.2015.03.030', 'pub5' => 'Bernardino, R. and Paias, Solving the family traveling salesman problem. , European Journal of Operations Research, vol. 267, issue 2, 2018. doi:10.1016/j.ejor.2017.11.063', 'email_publico' => null, 'foto' => 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u72gOrfXpR7NoaqnZJbIy5VvrTvs/zhQRwPSsCw1SWRuRkda1bW/3SA9GpOxfM2WHQJIFI61Hc24wDxTbu53MGzn3qrLfhicdqh2KvYhdArEjn8arzAFc/oKq3WtpFIVMZJHcmsq48QAttVSM0ezbE6iRemhUEkDk1h38YUnHWnPrgC8AFs8jNVp7xLhCwwD3FNQcWRKaaOM1ZR9oJHU1UtmxKCDzV7Wf+PgYrNiH7xQeDnFd8djzZr3i9dANlgeOtZrsAcCtBsnjAxVGePZKQfTNUiGRhsMD271PFK2flbAqscg8DgGpoTkZx7VRJK0spPEtIZJf+elOEeT1qFgQxosgJBNL/wA9P0oqsXbPXtmiiyEfaPOTnv0rC8SgiGxBbnzpP/QRW+wzgjpg1g+JiPs9iSP+Wsn/AKCKwlsVHcd4UJOhLH/euXU/nVL4ka0NO8NXAjfbNPiGLHHDct+mas+FpVXRJJGHyRvI31ywH8sV5n8UNZ+2atDZIQVt4w0g/wBs9B+WPzo3LOKefCnk/MxOCegqjeyiVmC4whwMdzSs5CmQ4yT+VT6RbrcXOJEEgK5wema0vZBa7sYT/e96dEhkb2Har2sWD2d1IqqBGORjnHtmk022Msyr2BBo5la4nHU6jSbYRWSKRg4ycVpm2W4QIwO3uaZaxYRV+lX8+WuMVxSlqehCNkJDp1rCB8g465NPfULa3Pyopx6YGKrXk7eU3l4LY7Vn2ltHNFOt3zMyny/7oOOKIpyerKlojZOuwlApEAzx80nI/Col1G3mYbwFxyCGBxXNJBIsMtv9nUmSQESZIIxngdua6xNF09NDto2eIXwGS6MMn2NaSgl1IU7dC0jJjcjdfSrdtMBJnvXPxbrePbvyc4K9a0oHPmge1c8jeJqXkoSIehrIe4G75DzmrmoqRZ7s9BXPxZnLqJNoXkgdWohqOTsh00UUkjNLKPcelWIbTTtoJeIkjjLdap6zYXul6db3LZDz7sIq8KAM5JrG3SpeQQvcJIrKjZjckDd257jPNbqnK25zc6OnmsLSVSvkpgenWub1TSzES8Ln2HarNw9zo18kUspliPRx7+tXZmEyjuCM1LbjuVo1ocHqRP2iNn69CKpJFuvQo6ZNa2vQFJ0IHWq2mKr6k5IJGCcfjXXB3RwzWojR4RiR04qjdICY2GeVKkVu6hbrHujU5xhjj8P8ayJV3QSj+JDkfQ1RLMvdxjGcGp0YgD5etQkDccde9WF+6KbYkriibafun86hdicnFPb71ISM4pXBoiOeOOcUUpXc9FVcVj7R3cYPasLxMT9ksj/dlk5/4D/9atkkD5hyDWF4mP8AodmAePPkOf8AgNYSegR3MaLV107QFDcbZnlZSfvBcFR+J/QV4pql5Lf6jJIzb3dzlh3P+f5V1vinVSts0KnKkMijP8WQSfw6fnXG2HN0Gx905xRTRYy4j2EIeSP6Vr+F4w11MScAJ36Cqd4gaVmUZBJJJq7pAC21zgEbiImb0DcZpz2NKe5D4gnFvYxWQAJaTzmPc5HAP0pmiQAknGOh5qrrJ83VTkkgnj25ra0xAseOnapekCt5m5agBh3xV9Lbzmz2qjaj5hitiGQIMYyTXKztiRGyjxlRkn1FQtp6OSMY47Vo4JGRxRtbNTc0sZotlhXYFLD3qBrVdxbZg+ta5Vyfu8UG2B5PNVcXKZC25+UhQAOuO9WIVKsWOc5q5JGAOFwBVRWMk/yDipvcaRoXQEmmtnrXNRIUfIyCDg4FdQY99myHgkVhNEUuCpBqIaMc46CTyPfWhgupS8fVcqMqeOlZqWcVpdtPEm7nKgjoeP65rbS3HoCDVgWMZw2T9K352ZezRzVxbTX8mZBwTmp0tngUZQ7eldD9kQLx1qNo1ZMMMEVEp3WocttjifEdt+7jbH3TXP6UQtxMx7Dn8xXWeIVxaMCeRgZ/GuTsz/pEmMYeJuPfGf6V10XeJw1o2dy3duHlVgM5HTNZpQ+dgn7ykH61K7lgpBxwaSUfuxOBkpggeta9TJamPICD1GD3FSJ90CnXcQSZtp+U/MPpTU6CmStxGHPFIcfj6089cCmHrQMYOHNFIT85oqiT7FSTGOhyDxXOeLpvK021IJ/1r9fda2wRvA4rifHlyzWNnBG/7yaR0QfUYz+ZrnYR3PLNauPNJdmBL5YAe5/rgn8qoWMnlSlgO3U1Jq7j+0JUAwkYCL9AKqwnaevXitIrQpvU15yPs55yT3q3o5T+yL3P3i8RH5nNZl1Iywxw5B4ySKmsmMFpMM4+ZBg+/H9KckXB2K17EZJppcHj/IrX01gUH50142SxvCFDIcDcfUdB+dPtovs8zwg52EflispaqyNYrW5u2Rya14scGsaybA981t2oDJlh3rlaO2CLccfyZzU8UIxk81GgAIGKuRDIFQjYY0BZflH4USww28e+ZsEDkVbLLGnTmsXVZGeM+9NtAU7q6e6ysK4QdDS2EQTAbrnmmvLJFaAWsIllx36VU0y+u3l2X1uInBJBXoaIols6We2aJI92PnXcMc8Vz96Ql4jDp0atC4vBFblmbaoXqTxWJZXiX0pDRyBWJ2sw4NPlDmubcdthQeqHnNOEfUCrGknzLVo2wxVytTSxBWOOKmw0UWwFPUGqNzLtXHf1rRmArIvRz7UrDdrHLeIZM2kxPQAEfWuZIEaQyJgEAFseh4xW34jl22roDyzDFYcJL25Qjow/mK76OiPKru8h8kXyKcDb1pYwXi2EHHYCtCWNI4kVhmqse6KXd6VqzNGLOpMQUr80QwfcZ4/nUadAK0NQjWG7z/yzkGPwqlFhZkB5XPNHQXUY4w2BTAvzZNTSEGc8cbs1EepoAgf75op+wFzRTIZ9Z+aozjqD1rg/Fj+b4r02NDuWG1d1HvuOD+ldfuBDLgjPvXG6w27xPFIwHzWRAA9nIP8AOsWKO55bqgP9ozbs53n6VWib94Cc4zWn4ijEOqzBAQrEN19RmsxOM4/nWkdiupZXM0wz0xVlJN8+R/Een48VXtYy2184UZzRDOsVwsjjK7uR7UMtLU7XTtOea3Xzl/cqSwHq3UZqjOrDVpz0BIOPbFdPYSpfWERtAGRx8xHO0d8+4rF19YrfV41Rl3FAHAPQj1rlbfMdnKuXQksQMn1BraiYrisWw++T1BraQZUetZS3N6bZoxNuAqyjYqnBnbUxbahJqDYWeUscA1SnO9CDzSvKTkk4qr5oYkk0rXHewkcLA/KxUn0NWpLUtGueSO9VhMFfAwan+1PgYQn6VaTRL1IZbJtm4/NjoD0qvHJIE8oRIMHhvSr7XO6PDDGfWqQAL5FDbQLQ0tMIhyvdjkn3q/cHdjkVhJNscYP51orNvj+lZ3KGTZwT6VialJiM1rTNgHiue1SbIIrWCuZzlocdrb+Y49Ebis203S3ODgA4JH0Oa0tRG63kI5YEn8qx7V9s28g4ZcH8a7YKyPKn8R0MwLCU55jIOPUVVYO21zgZ4q4/zWdySewx9Kr5UwFMng5zVMRm6lEZLYMMnZ7+tZgGQrjoevtWvOPNMkY4BBA+tZe0oxB6H+dCEMf72Mc0zuTUuw/ezmkC89OtAIrk7ZCR3oqRowZOTj6UVRD3PposCW5PJAPPauU19mGo6ZctxCqTRE/7XBA/MCukcjBG7Cg/lWRrluJtAtgiFm+1yYP4Z5/KsmENzzvxTCv+iyhRukjOfruOa5nB+gNdX4m/eWtkn8SvKB7jIx/WsSWxMMeZBgHkGqi9C2iIfLZp6scn2xVS4J2qccGp5H83OPur6VVkbkbRnHTPaquBoaXLqNt+8tpLhITlZDG/Ud+OvbNat+tzHeaZPcoUkkthFJuByzxuykn3I2mse9+S98+1aRY3wUbPI4GRx75rr59E1PUfBEWsy2z7bK4d2mc5Lo+AzD6FQD9afKmik3cs6e2GArciOFFc5YOAV5yDzmt+Js4wa8+adz0KbNO3UEA81LcDEaD+9UVuxGBVi72lFPpWT0NjIvpFt4SWIz0xWNDdGZwTnk8Ad62r2COaM5XPp7VhxrJazKVYYjzhSOtaQsO0uhqwWN1KjyoigJyQx5x9Ku/2bqEMDXH2aTy1HLx89az7bWrmCaR5Ig6PgELwRXRxeIYLi0EO91zjcp+UE+/rWiM5Oouhz0qXUanzIZh/tFDiqZu9h5PGcV0upa7axxAMPMAI+VcZrjb29a4kmYxbSWBRvbHem4pjjUm90X1mWRhgitSzcspWsLT4M5cu2T2NdHp0XEjnGBwKwmkWirdSFVbmuYvnJDNyea3dSkIk2r0JrltWuDBbMQTnB6dc9v51pTTuc9WVkQ6vp/kaCt0rq6NJ5ZOR94ruwPoP51y4QKsR9a2NcEFpP/ZyT+Ytr8ny8jefvE+5P5DA7Vj7T5cYJ7nmu1bHnSd2bgcPpsuCc8Go2GIPM69M80QnNi4xwV/rUcPzQOGzjGRRuMic7pJGYgAjIwKzpPmdht6dKtsT0OeeKpXYZJflPUAgf5/GgljSy5A24zSjYR1NROTjdxSLI6ggEc9qdgvYc8JMhKlT+NFQspZi2PzFFBNz6PMhBHTjqKq6jJ5dtCAC6GWRiR2O3FJcTLCu5mHy8n2/+tXOa14gt/sKxqxWRZy+R0IKkfzrK4Q3Oc1eSJ71EDZ8tS345Ax/Osa/u3uCIgMkcKB2pt1eiSR2wXc5+c9hVBJSHJzyaqJox8oMKcgZqqW+brg+9Sys2e5qJjuQ5HPWrSJZraQftdwlmY2cXLKiqo+ZWPTFeoX/AIutftE3hC2jY6dBp7Wg9WmXBb68A15VpF2bCf7cvMtuSYvZz0P4VXsJpUv7Z0Y+Z5q4Ykkk7uc/maLjOq06V1hjL9hg49iRXS2coYe9c9ZrFNCwiYlTI5BPXBORWlp0rJIY2HIPX1rjqLU7aTOmjbABBqWaRmQccVSjf8qmLFo9pOK5mjqQxxleeKoT24bPJ+tXckcdaXaGHNNSsa3KMMIGQwyKlktIGUdPpirTQgYIqNo2HNaqoHMzPmgjVfkT61nvas7DK8VuOhI9BULR4PWn7QTm7WIbdPKjAxW1azKLRk/i71jnJbr0qyZVigOPvEVlJGZQvHDSu3YVztxg6hHPKhe2tpRJKAcZCguB+O2tm/lW3iYk8dT3rMjtZh4V1OWWM4uI5JVyOSqlI8/m7D8K66MG9Tkry6IwbnR5UGq3c6skUExQHP3pXJZFz9NzH2FZmPkQtnqeK7DWLy71Dwbo/lWqpZxM8csynJkuMAEt/wABAA/GuUCgiQf3DnpXU1Y5LFqCYrbsDjAwKdEOOP8APNVUYrDjrzk1YSQ+YASAv/16yuUQToDK55xQI4pQpdAWxgE96fJs+fDZOaVJbdMrIm760pNoVkWILG0ZQGiB55zmri6bpxxmCPPTlj/jUVtNp5XBt48eu2tFJNHIA+yQk47x5qOaXYTRVXTNPGQIYh9Tn+tFX92j45sbf/v0P8KKOaXYVh91qd3cRnzJmkAPEcY2r+Pc1z19NIw2SHC9dg9a0bi+u9xRmIz0461izFpJcyZAPJyK1SCKsVZGyeOPbtTOQRjrVlgo7ZA6VA2MfKDz7VQWGs579aYowcnvUrKI/vDLEcUqKW5/SgRPAsYiKP8AcY5Jq3Lb2mnTCWHUYbxlwU8pWAB98gciqLAqdvQ02VSI8nOaCrHXaDg2qdQCSP1rSnRkImTOV6/Ss7w8CbIZHRjW8UyoNcs9zqpktpdLJEvzYNXElBXrWJ5TQSfKflNWllJ57isWjoUjSRgTipscCs+GcHvzV6GUMMGs+U2jInQEjpn2pXjB6qRipYZFB3Ec0GUMeRVpFJmdLxxjjNQt1z2q3cbCcAVRnkCjaMUWCUkRbgXOTgVG0vmSk7uE6VVkuDv2r1qNRJcDZGcIPvP2o5TnlMjF1p0urxrqjSpYKSXMS/O2Og9snv2qODXbbU9Q1iCSLy4bjTpLaxhH/LMKQ6r9TtJz3JrM1NFS42J90Kee9c/JLJA6TxuVljIKsOoI7/0+ld9N2jocVTVm3p3iMp4S1HQyAUknS7ifrhhww+hH9fWsQMUuJF7MoB+lQvcJLIZEi8tm4YKcKOe3t7VNdjbcAjPzKP5VTZkmDjbHSseA1SlfNtN46r1qqx3RjuKzKJcblXtnqaekUTud5P4Gq6k+VxxjvVWW4kHIbHpx1qrNolnSRWtuqK6S7m/u7uKvRxRnBwM1xiX9wh+WQg+oxUo1a9HS5cUlBktnaLFH3Aorijqt9j/j5k/OijlYrnqcOmRRZluAskvrjhfYVlanYQFEkKbWZjj6VuBy3cDHOKy9Yi823UrnqfmFErpChK7OaewjVA5w2CeMVTkiijiaVo1Ug4RR/WnyXMsWUDkjPeqztvOWzk96I7GrKxUu5YnJqxEhBAx1pUiUnPb6VMXVSFGKpsEhiQtLIWx16GkuISIhnoa0UuIvLESKB6mq1xILgZA+VRtA/rSW49Dd8N/6h09G9a6eNeMYrlfDZKiTgcnPT/PrXXRj5BXPU3OmmQyw8YGOariAhsqa0wo6GkSHBxjrWdzSxnbc8k4PrU0UjIf71XWsw44HNQNaSxdDxSdi1ckF3kYPFKboHAziq5SQD7ufwppjlb+D9KEVzMlluFI+9WdPN8xC8mrRs5CuXwBUkOnNM2Pur3NDsiG2zMtbB7qQDJC92rVkhSCDZGAoA/OtBYFt4gidPXvVK6J2kevrS5mxcpw+ttslJAySDzXPy42DPpzW/r4xc85CsmR+dc4zDaBkjqK7Kb0OOpuVSdjDHIzWld4Koc9yv5VnN8rn2q9MQYCRzja/5itGZIdbOUJJzsIptxAYGKD7h5U0iHEakfdBP5GrFvIk0f2eU/7rGpLKiYww9RxVO7XHHPBq+YJbecq/YHr6VTuOWNNENMpLnPA71IR6imAnJAp/PTAP41VyA4IopMPjG0fnRRcR7IGHp+lRtg8uu5MkYH86TJznIqzaczx+gJpT2Jh8R57qMWLp/LyVVjjioAsx+8EUD1rrNW0pZp5HiU7lJzjoa5ya1MUm0j5h1BHFQtjp3Kx+RePmb+VVd+0kt1q5KARgy4/2QMVU8slj6U0gFVyOnHetNbZ/LWJAS20dvWqthEHO9hnB49K2o5ijkp1yoHPYdKG0gtdl/TrKWwne2lx5kblWxyM5/wA/lXTQ8oCawoUZJI2YksQSWJ6k8k/59a3Lc/KCK5pO7OmmrE5xjipoxu7U0JkZAqWNeelZs2RYjTA6VKYkYDKimRnHUdKnDBu1SMiNsuOBTGhXstWwRjANMY+1IZRNsGbLDj0qdY1ReFAHtUm3PrTiuUpAinKABWXc5ZiPyrWnAA6VnugyWprQLnE+JosPA3swNclICVIxyK73xBB5sT45I6Vw0sRU7/4ScH2rsptWOKrHUgcKYWI+8BzT0k/dR+hUxH8P/wBdNUAq+R2FJz5Tp3BDj69/6VsYbC28uAyNznNTKPaoYj1JHOafAkjTYTk5wRSZVy3JdmSJUkXLKMbvWs+dQ/3OvpWs2mSBRz15JPaqc9rLEudp3Z9M5FSmF7mX5DockdfelIwoJxnPpTpItj71XhhkdqQDjpg1T1IaDH0opxUnPFFIR6vuB4BGfpVi0kVHV3ICjNYj38nRB171XaSRxguTTbuEYNGtc3sazylPm3NkVjXhNwrFwBj0FSogCjuaeYlII25z61BpY5mSKIsx39D3pPMQHESZ+tXLnRZTMxToeetaGhaAbu/trcjLSSKv0/yKtICta6FqU1o89vbO0SAkkdKfpmk3bXQkmQqo6g17Pd2ccWhXYiRY41tmCIoxgZ/nXFSQhOQADjisqrsawVyg64dAOlaFpn7tVWXMi561ZhO1xWFzeJqRKfwqXYR0ptuwKirXlHG4YqWzRISLAHIqwm0jpUAHNTx9KkY84xgAVGRzUobJx0oxg9M0MAVQoyaZK4xwKkIJGcVE4OCaT0GUZQc5NUpehxV6XJJzVKYcGlcRkTxLNKU7NxXGXMMdtqsltMuI5D/PjNd9FGHcnHGa5HxfaMl/FJGOSvH1yf8AGt6ctbGVSNznrnTpbWdkKEjoG7GqhRoj8w5zjH0r0PwYbLULkQ6uqbIUOGc4zxgcd8VyevLC+qXTQDEZkIU54Iz+tda8zlkkYnU4C9a27O0ZCsrDgdhWctmzQtJzheRXRWOJbVDngjBNKTJRZVNowOR/Kq9zZeZGxjbYQcgVeAIGODxSEHsKxGZMSwXbQ+bCpJBVgfUU6Xw3ayZ8suhPPqKVIfL1by+dhIYVscKOp4p3BpHNv4XkziOeM/UGiumLgHLCii7FZFRE2kdealCj+7TkXjpzUgFaWGIAMdKUjnNB60rkhQBQBXwWJ7c4rq/AVpv1trnb8sERbnsSCorl8Ag+leieA7cLplzOeGkmCH/gIz/M1aJZ0F7GW0e5QDJML/415/MSzjHSvSVXMJQ/xqy/nXnGw4KnqOD9RWFU2oasrCMmUcdKnMZBBxTo4zvBzVrywVrG50JC2j1qAZQEdayIBskwTitWNsipZaF2Z61Ii4HWmnkjmnrmpKJFVe9PyMY4pgpQaYgZ/lqsxPIzUzEHimOMDNJoopzECqE4zwD1rQkTNU5IyM1N7CZDEm04xWdqWnf2je28YTexygUdya1VRga6fwto2XGpzx44xAD6Z5b+lbUotu5lUkkjE1bwU1joZjsbW1kkjUyS3EgAZOOcGvLm08yy7pOnWvefGNz9l8KXpzhpUESn1LHn9K8bZDnkY7f4V2s4Oa5S+xIbdoxkBhjipoLI2rAR5MbD5h6fSrSLyoA4FSMQOCMfSs2NMiwRx3oWPPXNSbe460dv51NiigIfN1Ayf3RireCRg9Kckaqcjr3xSgVIDDg9eRRTiPaiiwDxwaYzY4HWnPgYpq8t0zWyG9xyj86RzzipQOvFRMBnOeTQxMRUDOB0HevUvCsBt9CtVIwZB5pHf5jmvMAoJzk8CvYbCIRQW6DtCg/JRVImRcCfMDj7p4rh9Xs/smpzxkYRz5iH1B6/rXoIUFc4OKyNd0s3loHjXM0QyvuPSonG6KpT5WcWkQxxVpIzs61GnHB4OcEelTBgF4rkZ3LUplMTZGOK0oBlQSMVTKAmrcWcAZ6UmVYnCEtwaeI8d+aUY4NBOTSGCrmjGGpVBwQaULgHmgLDeCelRvUh4FQueaGMrv1qBlB6mrRHFbGkeHnvGWe6DJb54Ujlv/rU403Iic1Hcq6HoLX7rPMpW2U/99/Su1EYGFUbVGPlHQe1SLEqIqooVQMAAcAUN6D04967oQ5UedOpzs8/+I95mOysV4B3TMP0H8zXnZxuxjB966jxpeC88RXGDuSHEKke3X9TXNhRmhsSQxfmPsO9DjkCpNgC45pjelSx2EQUuRQq8A0oUYz2zUsaEII/+tQF/WnbR6U7HFJAR7c96KeqjBzRQBCx3E57U6LBqpaTCWHk8jircPJ5qy5q0miUdelRMnzDA5qYj3phGCOetMkVVIJ9hmvZkGIYmH9xf5V45HgnGfrXsNk3m6ZayA/egjP/AI6KtEs0ocMg9+tK6ccd/aooG4xVlSD1pmdznNV0AXDtc2wxIfvp0DVzckLo5RkKsp5UjBr0kp3qreWEF4uJowcdG7j8aylST1N6ddrQ4FI89RU6qABit+fw26ndBMCP7jiqUmlXkJ+a3fHqOa53SZ2KtBlMdKFGTk5qb7JMp5hlH/ADT0tJc48qXn/YNTySK9pDuRjJ45xQRx6VcTTbuQfLbyY9xirUOg3jff2Rj3bJpqm2S6sV1McofSiK1luZBHErOx9BwPxrqIPD9uhzMzSn0HArUihjiXZFGqqOgUYraNDuYSxKWxjab4fjgZZbk75OoTHArbCkAY4A4FOxj3pC2Ae9bqKicspuW4jEAEduwqnqN4LGxnumOBEhb8R0qwxyc965Tx5e/ZtJjtlb555Mn/dX/wCvTbJSPNZ2Mrs5JZiSxPqT1qPGFzgU8gGhuvI4ArNs0IeAMAdajAJapJOTmhB271LGIF+XFIB044p547U0fTpSAVBnsaDjOBTv4cZwaAOuRmlYQ0DnJ60U8D3xRVWQHPWBIuplzxnpWzDRRQtjoxH8RkxqJ+gooqkYkqDAFet6Ec6BYk/88R/M0UVaJZowd6sDrRRVEInjOSM06QDNFFJiEwN1OHHSiimId2p2OaKKkYuBjv8AnUe47sUUVYkSADNNYnJHYUUUgQ1j8gqPtRRSGCcuPrXm3jx2OsxoWO1bdQo9OaKKmRUTlB1pD9w0UVmUyBhT06/hRRSGNPLUCiigBzE8UKeaKKCQX7xoooqhn//Z', 'nome_foto' => 'ana2.jpg' ) $foto = '<span style="color: rgba(44, 63, 177, 1);">ana2.jpg </span>' $foto_exists = true $nome_ficheiro_foto = 'ana2.jpg' $cv = '<font color="red">Não definido</font>' $cv_exists = false $orcid = 'http://orcid.org/0000-0002-1339-1974' $researcherid = 'http://www.researcherid.com/rid/M-1764-2015' $scopusid = '' $google = ''include - APP/View/PaginaPessoal/index.ctp, line 373 View::_evaluate() - CORE/Cake/View/View.php, line 971 View::_render() - CORE/Cake/View/View.php, line 933 View::render() - CORE/Cake/View/View.php, line 473 Controller::render() - CORE/Cake/Controller/Controller.php, line 968 Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 200 Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 167 [main] - APP/webroot/index.php, line 112
Interesses Científicos
Problemas de determinação de rotas
Escalonamento de viaturas e tripulações
Rotações
Modelos MILP
Heurísticas
Scientific Interests
Routing problems
Vehicle and crew scheduling
Rostering
MILP models
Heuristics
Publicações selecionadas