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An Optimization-Based Decision-Support Tool for Post-Disaster Debris Operations
Indexado
WoS WOS:000403245100008
Scopus SCOPUS_ID:84991577410
DOI 10.1111/POMS.12643
Año 2017
Tipo artículo de investigación

Citas Totales

Autores Afiliación Chile

Instituciones Chile

% Participación
Internacional

Autores
Afiliación Extranjera

Instituciones
Extranjeras


Abstract



Debris generated by disasters can hinder relief efforts and result in devastating economic, environmental, and health problems. In this study, we present a decision-support tool employing analytical models to assist disaster and waste management officials with decisions regarding collection, transportation, reduction, recycling, and disposal of debris. The tool enables optimizing and balancing the financial and environmental costs, duration of the collection and disposal operations, landfill usage, and the amount of recycled materials. In addition to post-disaster operational decisions, the tool can also support the challenging task of developing strategic plans for disaster preparedness. We illustrate the applicability and effectiveness of the tool with a disaster scenario based on Hurricane Andrew.

Métricas Externas



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Disciplinas de Investigación



WOS
Engineering, Manufacturing
Operations Research & Management Science
Scopus
Industrial And Manufacturing Engineering
Management Of Technology And Innovation
Management Science And Operations Research
SciELO
Sin Disciplinas

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Publicaciones WoS (Ediciones: ISSHP, ISTP, AHCI, SSCI, SCI), Scopus, SciELO Chile.

Colaboración Institucional



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Autores - Afiliación



Ord. Autor Género Institución - País
1 Lorca, Alvaro Hombre Pontificia Universidad Católica de Chile - Chile
2 Celik, Melih Hombre Middle East Tech Univ - Turquía
Middle East Technical University (METU) - Turquía
3 Ergun, Ozlem - Northeastern Univ - Estados Unidos
Northeastern University - Estados Unidos
4 Keskinocak, Pınar Mujer Georgia Inst Technol - Estados Unidos
Georgia Institute of Technology - Estados Unidos

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Financiamiento



Fuente
National Science Foundation

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Agradecimientos



Agradecimiento
This research has been supported in part by National Science Foundation Grants CMMI-1000085, CMMI-1034840, and CMMI-1538860 and by the following Georgia Tech benefactors: William W. George, Andrea Laliberte, Joseph C. Mello, Richard "Rick" E, and Charlene Zalesky. The authors are grateful to the senior editor, the referees, and the special issue editor Professor George Shanthikumar for their constructive comments and suggestions.
This research has been supported in part by National Science Foundation Grants CMMI-1000085, CMMI-1034840, and CMMI-1538860 and by the following Georgia Tech benefactors: William W. George, Andrea Laliberte, Joseph C. Mello, Richard “Rick” E, and Charlene Zalesky. The authors are grateful to the senior editor, the referees, and the special issue editor Professor George Shanthikumar for their constructive comments and suggestions.

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