Pre-positioning of Emergency Supplies for Disaster Response

Abstract

Extreme events such as hurricanes and earthquakes can strike a community with little or no warning and leave high levels of devastation behind. Emergency response providers require large quantities of resource in the aftermath of such events, but these may be limited because of lack of preparation. In order to provide immediate assistance to disaster victims, essential supplies must be strategically placed before the event so they can be accessible after. The main goal of this research is to develop a large-scale emergency response planning tool that determines the location and

On the appropriate objective function for post‐disaster humanitarian logistics models

“…This provides the analyst with a parameter, the penalty, which could be used to tweak the model to produce reasonable results. There are a number of variants, including shortage costs/penalties ( Barbarosoglu and Arda, 2004;Yi and Kumar, 2007;Yi and Özdamar, 2007), cost on arcs representing the flow of "pseudo supplies" for unmet demand (Rawls and Turnquist, 2010), and penalty cost for a fraction of unmet demand of different commodities (Balcik et al, 2008), among others. These approaches are relaxations of the hard constraint models discussed later in the paper.…”

“…These are special cases of penalty-based models that incorporate hard constraints, such as "equity constraints," to try to ensure that a commercial logistic model produces reasonable results. A fairly wide range of constraints is used, including: demand satisfaction before deadlines (Han et al, 2007), and flow conservation at nodes where amount supplied equals amount demanded (Rawls and Turnquist, 2010), among others. Mathematically, these models are equivalent to a constrained minimization of logistic costs, as shown in Eq.…”

Location and Emergency Inventory Pre‐Positioning for Disaster Response Operations: Min‐Max Robust Model and a Case Study of Yushu Earthquake

“…Each of them happens with a probability p s for any s 2 S. Applying these notations, we obtain the following two-stage stochastic model: (6) does not explicitly have the second-stage decisions for each scenario s 2 S, the instance of Qðr; d s ; q s ; u s Þ yields an optimal secondstage solution that exclusively corresponds to the second-stage decisions for scenario s. In other words, model (6) allows different scenarios having different second-stage decisions. We also note that model (6) can be viewed as a single-commodity version of the model proposed in Rawls and Turnquist (2010).…”

“…As pointed out by Tufekci and Wallace (1998), treating these pre-and post-event responses separately may result in suboptimality. This observation motivated the consideration of integrated location, inventory pre-positioning, and delivery for disaster relief in the recent literature (see, e.g., Bozorgi-Amiri et al 2013, Campbell and Jones 2011, Dalal and € Uster 2017, Davis et al 2013, Mete and Zabinsky 2010, Rawls and Turnquist 2010, Rawls and Turnquist 2011, Salmer on and Apte 2010. We refer readers to Altay and Green (2006), Simpson and Hancock (2009), Galindo and Batta (2013), Anaya-Arenas et al (2014), and Gupta et al (2016) for excellent reviews in this area.…”

Optimization of Temporary Debris Management Site Selection and Site Service Regions for Enhancing Postdisaster Debris Removal Operations

“…Many of these studies have focused on either predisaster planning or logistic operations in postdisaster relief missions. Recent predisaster studies, for instance, have developed models for resources preparation (Chang et al, 2007), debris prediction (Hsu et al, 2016;Miao and Ding, 2017), and prepositioning of supplies (Rawls and Turnquist, 2010). Previous studies have investigated routing assessment (Hobeika et al, 1988;Huang et al, 2013;Brooks et al, 2016), rescue team deployment (Zverovich et al, 2017), evacuation planning (Guo et al, 2012;Zverovich et al, 2016), ambulance and resources distribution (Sheu, 2007a;Fetter and Rakes, 2011;Özdamar and Demir, 2012), relief demand management (Sheu, 2010), and human behaviors (Nejat and Damnjanovic, 2012) in postdisaster scenarios.…”