Localização de centros de auxílio e distribuição de suprimentos em operações de resposta a desastres
DOI:
https://doi.org/10.14295/transportes.v25i2.1168Keywords:
Desastres naturais. Logística humanitária. Modelagel matemática. Programação estocástica.Abstract
A localização de centros de auxílio para o atendimento das vítimas e a distribuição de suprimentos essências à sobrevivência são operações chaves em situações de desastre. Embora muitos trabalhos da literatura tenham desenvolvidos modelos matemáticos para auxiliar em alguma dessas decisões, poucos autores se preocuparam em integrar ambas as decisões com o dimensionamento da frota na tentativa de gerar soluções mais eficientes. Nesse artigo são desenvolvidos dois modelos teóricos de programação estocástica inteira mista para apoiar as decisões de localização, distribuição e dimensionamento da frota de forma integrada minimizando o custo total esperado. Para resolver os modelos, foram exploradas variações de métodos híbridos que combinam métodos exatos e heurísticos implementados no solver CPLEX. Os modelos matemáticos foram analisados com base nas informações do megadesastre da região Serrana do Rio de Janeiro de 2011. Os resultados indicam que a configuração adequada do método híbrido pode retornar soluções com até 34% de melhoria em relação à opção padrão. Os modelos mostraram ser potencialmente úteis para o apoio às decisões dos órgãos encarregados das ações de resposta a desastres.
Downloads
References
Afshar, A., Haghani, A., (2012). Modeling integrated supply chain logistics in real-time large-scale disaster relief operations. Socio-Economic Planning Sciences, v. 46, n. 4, p. 327–338. DOI: 10.1016/j.seps.2011.12.003.
Altay, N., Green, W., (2006). OR/MS research in disaster operations management. European Journal of Operational Research, v. 175, n. 1, p. 475–493. DOI: 10.1016/j.ejor.2005.05.016.
Apte, A., (2009). Humanitarian Logistics: A New Field of Research and Action. Foundations and Trends in Technology, Information and Operations Management, v. 3, n. 1, p. 1–100. DOI: 10.1561/0200000014.
Bozorgi-Amiri, A., Jabalameli, M., Alinaghian, M., Heydari, M., (2011). A modified particle swarm optimization for disaster relief logistics under uncertain environment. The International Journal of Advanced Manufacturing Technology, v. 60, n. 1-4, p. 357–371. DOI: 10.1007/s00170-011-3596-8.
Bozorgi-Amiri, A., Jabalameli, M., Mirzapour Al-E-Hashem, S., (2013). A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR Spectrum, v. 35, n. 4, p. 905–933. DOI: 10.1007/s00291-011-0268-x.
Busch, A., Amorim, S., (2011). Tragédia da região serrana do Rio de Janeiro em 2011: procurando respostas. Rio de Janeiro: Coordenação-Geral de Pesquisa da Escola Nacional de Administração Pública (ENAP), v. 1, p. 1–20.
Chang, M., Tseng, Y., Chen, J., (2007). A scenario planning approach for the flood emergency logistics preparation problem under uncertainty. Transportation Research Part E: Logistics and Transportation Review, v. 43, n. 6, p. 737–754. DOI: 10.1016/j.tre.2006.10.013.
Danna, E., Rothberg, E., Pape, C., (2005). Exploring relaxation induced neighborhoods to improve MIP solutions. Mathematical Programming, v. 102, n. 1, p. 71–90. DOI: 10.1007/s10107-004-0518-7.
Dolan, E., Moré, J., (2002). Benchmarking optimization software with performance profiles. Mathematical Programming, v. 91, n. 2, p. 201–213. DOI: 10.1007/s101070100263.
Döyen, A., Aras, N., Barbaroso, G., (2012). A two-echelon stochastic facility location model for humanitarian relief logistics. Optimization letter, p. 1123–1145. DOI: 10.1007/s11590-011-0421-0.
Eshghi, K.; Larson, R. C. Disasters: lessons from the past 105 years. Disaster Prevention and Management, v. 17, n. 1, p. 62–82, 2008. DOI: 10.1108/09653560911003705.
EM-DAT., (2016). The international disaster database. Disponível em: <http://www.emdat.be/advanced_search/index.html>.
Ferreira, D., Morabito, R., Rangel, S., (2010). Relax and fix heuristics to solve one-stage one-machine lot-scheduling models for small-scale soft drink plants. Computers & Operations Research, v. 37, n. 4, p. 684–691. DOI: 10.1016/j.cor.2009.06.007.
Galindo, G., Batta, R., (2013). Review of recent developments in OR/MS research in disaster operations management. European Journal of Operational Research, v. 230, n. 2, p. 201–211. DOI: 10.1016/j.ejor.2013.01.039.
Hutter, F., Hoos, H., Leyton-Brown, K., (2009). ParamILS : An automatic algorithm configuration framework. Journal of Artificial Intelligence Research, v. 36, p. 267–306.
ILOG, (2011). ILOG CPLEX 12.1: user’s manual and reference manuals.
Li, L., Jin, M., Zhang, L., (2011). Sheltering network planning and management with a case in the Gulf Coast region. International Journal of Production Economics, v. 131, n. 2, p. 431–440. DOI: 10.1016/j.ijpe.2010.12.013.
Lin, Y., Batta, R., Rogerson, P., Blatt, A., Flanigan, M., (2012). Location of temporary depots to facilitate relief operations after an earthquake. Socio-Economic Planning Sciences, v. 46, n. 2, p. 112–123. DOI: 10.1016/j.seps.2012.01.001.
Mete, H., Zabinsky, Z., (2010). Stochastic optimization of medical supply location and distribution in disaster management. International Journal of Production Economics, v. 126, n. 1, p. 76–84. DOI: 10.1016/j.ijpe.2009.10.004.
Moreno, A., Alem, D., Ferreira, D., (2015). Coordenação da localização, distribuição e dimensionamento de frota em situações de desastre. In: BRAZILIAN SYMPOSIUM ON OPERATIONS RESEARCH. Proceedings… , p. 1499-1509.
Nolz, P., Doerner, K., Gutjahr, W., Hartl, R., (2010). A Bi-objective Metaheuristic for Disaster Relief Operation Planning. Advances in Multi-Objective Nature Inspired Computing. Studies in Computational Intelligence, p. 167–187. DOI: 10.1007/978-3-642-11218-8_8.
Noyan, N., (2012). Risk-averse two-stage stochastic programming with an application to disaster management. Computers & Operations Research, v. 39, n. 3, p. 541–559. DOI: 10.1016/j.cor.2011.03.017.
Ortuño, M. T., Cristóbal, P., Ferrer, J. et al, (2013). Decision Aid Models for Disaster Management and Emergencies., Atlantis Computational Intelligence Systems. v. 7, p. 17–45. DOI: 10.2991/978-94-91216-74-9.
Rath, S., Gutjahr, W., (2011). A math-heuristic for the warehouse location–routing problem in disaster relief. Computers & Op-erations Research, v. 42, p. 1–15. DOI: 10.1016/j.cor.2011.07.016.
Rawls, C., Turnquist, M., (2010). Pre-positioning of emergency supplies for disaster response. Transportation Research Part B: Methodological, v. 44, n. 4, p. 521–534. DOI: 10.1016/j.trb.2009.08.003.
Rawls, C., Turnquist, M., (2012). Pre-positioning and dynamic delivery planning for short-term response following a natural disaster. Socio-Economic Planning Sciences, v. 46, n. 1, p. 46–54. DOI: 10.1016/j.seps.2011.10.002.
Salmeron, J., Apte, A., (2010). Stochastic Optimization for Natural Disaster Asset Prepositioning. Production and Operations Management, v. 19, n. 5, p. 561–574. DOI: 10.1111/j.1937-5956.2009.01119.x.
Shen, Z., (2007). Integrated supply chain design models: a survey and future research directions. Journal of Industrial and Management Optimization, v. 3, n. 1, p. 1–27. DOI: 10.3934/jimo.2007.3.1.
Song, R., He, S., Zhang, L., (2009). Optimum transit operations during the emergency evacuations. Journal of Transportation Systems Engineering and Information Technology, v. 9, n. 6, p. 154–160. DOI: 10.1016/S1570-6672(08)60096-3.
Downloads
Published
How to Cite
Issue
Section
License
Authors who submit papers for publication by TRANSPORTES agree to the following terms:
- The authors retain the copyright and grant Transportes the right of first publication of the manuscript, without any financial charge, and waive any other remuneration for its publication by ANPET.
- Upon publication by Transportes, the manuscript is automatically licensed under the Creative Commons License CC BY 4.0 license. This license permits the work to be shared with proper attribution to the authors and its original publication in this journal.
- Authors are authorized to enter into additional separate contracts for the non-exclusive distribution of the version of the manuscript published in this journal (e.g., publishing in an institutional repository or as a book chapter), with recognition of the initial publication in this journal, provided that such a contract does not imply an endorsement of the content of the manuscript or the new medium by ANPET.
- Authors are permitted and encouraged to publish and distribute their work online (e.g., in institutional repositories or on their personal websites) after the editorial process is complete. As Transportes provides open access to all published issues, authors are encouraged to use links to the DOI of their article in these cases.
- Authors guarantee that they have obtained the necessary authorization from their employers for the transfer of rights under this agreement, if these employers hold any copyright over the manuscript. Additionally, authors assume all responsibility for any copyright infringements by these employers, releasing ANPET and Transportes from any responsibility in this regard.
- Authors assume full responsibility for the content of the manuscript, including the necessary and appropriate authorizations for the disclosure of collected data and obtained results, releasing ANPET and Transportes from any responsibility in this regard.
