A scenario planning approach for the flood emergency logistics preparation problem under uncertainty
By Mei-Shiang Chang, Ya-Ling Tseng, Jing-Wen Chen. 2007
584 citations recorded by the source corpus.
Subfields: Industrial and Manufacturing Engineering, Ocean Engineering, Organizational Behavior and Human Resource Management
View Paper: https://www.semanticscholar.org/paper/644eaaa174b8b766098443970037e9ad55cb3774
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Industrial and Manufacturing Engineering
- Mustafa Alp Ertem
- Muhammad Salman Habib
- Young Hae Lee
- Muhammad Saad Memon
- S. M. Al-e-hashem
- Mohamad Saeed Jabalameli
- Ali Bozorgi-Amiri
- Linet Özdamar
In Ocean Engineering
- Muhammad Salman Habib
- Young Hae Lee
- Muhammad Saad Memon
- Mustafa Alp Ertem
- S. M. Al-e-hashem
- Linet Özdamar
- Emily L. Tucker
- Xin Hong
In Organizational Behavior and Human Resource Management
- Mustafa Alp Ertem
- Muhammad Salman Habib
- Young Hae Lee
- Muhammad Saad Memon
- Xin Hong
- Miguel A. P. M. Lejeune
- Emily L. Tucker
- Karmel S. Shehadeh
Citations in the corpus
- Stochastic network design for disaster preparedness (2015, methodology): “Chang et al. (2007) also focus on dividing the disaster area into several regions and assume that a facility gives a higher priority to the demand points in its own region before extending help to others.”
- A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty (2011, methodology): “…of emergency commodities and necessity items by probabilistic scenarios representing disasters and their outcomes (see, e.g., Cormican et al. 1998; Barbarosoglu and Arda 2004; Beraldi et al. 2004; Chang et al. 2007; Beraldi and Bruni 2009; Mete and Zabinsky 2010; Rawls and Turnsquist 2010).”
- Stochastic optimization models for location and inventory prepositioning of disaster relief supplies (2020, background): “Optimization criteria of these studies include minimizing the expected total cost (Chang et al., 2007; Döyen et al., 2012; Mete and Zabinsky, 2010; Rawls and Turnquist, 2010), minimizing trade-off of total cost with Conditional Value-at-Risk (Noyan, 2012), minimizing expected response time (Duran et”
- Mathematical Models in Humanitarian Supply Chain Management: A Systematic Literature Review (2016, methodology): “[16] Preparation and response phase (location-allocation model) Demand and location of demand Mixed-integer programming Sample average approximation scheme ESRI ArcGIS 9.”
- Models, solutions and enabling technologies in humanitarian logistics (2015, methodology): “Chang et al. (2007) use a scenario planning approach for rescue center location problem in conjunction with GIS software for floodemergencies.”