An Integrated Logistic Model for Predictable Disasters

Abstract

I n the aftermath of a disaster, the relief items are transported from temporary warehouses (Staging Areas, SAs) to the Points of Distribution (PODs). Reducing the response time to provide relief items to disaster victims and cost minimization are two important objectives of this study. We propose an integrated optimization model for simultaneously determining (1) locations of staging areas, (2) inventory assignments to these SAs, (3) selecting sizes and numbers of trucks, and (4) routing of trucks from SAs to PODs. We also introduce another variable, a value function, which forces the model to reduce the logistics response time. We study the interactions among these variables through extensive sensitivity analysis. The time horizon for supply of relief items to disaster areas is usually limited to six days after the disaster occurs. Therefore, we use the proposed optimization model in a rolling-horizon manner, one day at a time. This reduces daily demand uncertainty. We analyze three disaster scenarios: (1) a low impact disaster, (2) a medium impact disaster, and (3) a high impact disaster. We conduct 720 experiments with different parameter values, and provide answers to the following questions that are useful for the logistic managers: (i) What are the right sizes (in terms of storage capacities) of SAs closer to the PODs? (ii) How should the budget be allocated in a disaster scenario? (iii) What mix of different types (in terms of sizes) of trucks should be selected in a given scenario? The most important managerial insights include: (i) operational budget beyond a limit does not improve the operational efficiency, (ii) when the budget is very low, it is essential to select smaller SAs close to the PODs in order to carry out operations in a feasible manner, (iii) when the impact of disaster is high, it is always beneficial to select larger SAs close to the PODs (as long as the budget is not very low), (iv) when the budget is high and the impact of disaster is not very high, the emergency management administrators need to select SAs prudently based on the tradeoff between the operational cost and the humanitarian value, and (v) the cost of operations is higher when all the trucks are of the same type compared to the case when there is a mix of different types of trucks. Also, we find that the optimal selection of SAs is not impacted by different combinations of the types of trucks. The focus of this study is on disasters that can be forecasted in advance and provide some lead time for preparations, for example, hurricanes. In order to understand the disaster management process of such disasters and develop our model, we (i) interviewed several emergency management administrators, and (ii) studied the disaster management processes available in documents released by various government agencies.

Disaster Management from a POM Perspective: Mapping a New Domain

“…As an example of the latter situation, see the 2016 paper in POM on prepositioning of critical supplies for predictable disasters (Vanajakumari et al. ).…”

An effective two-stage stochastic multi-trip location-transportation model with social concerns in relief supply chains

“…Most studies that developed integrated models for coordinating preparedness and response activities in humanitarian logistics focused on multi-period settings. However, most multi-period models overlooked the uncertainty about the number of affected people and their needs (Yi and Ozdamar, 2007;Afshar and Haghani, 2012;Lin et al, 2012;Vanajakumari et al, 2016). Recognizing the inherent uncertainty in disaster operations, such as the needs of victims, route availability, supplies, and shipping time, various authors have proposed two-stage stochastic programming models, often representing pre-disaster preparedness as first-stage decisions and post-disaster response as second-stage decisions (Salmerón and Apte, 2010;Mete and Zabinsky, 2010;Ahmadi et al, 2015;Rath et al, 2016).…”

Surface vs. Air Shipment of Humanitarian Goods under Demand Uncertainty

“…Salmerόn and Apte (2010) focus on pre-establishing capacity for warehouses, medical facilities, ramp spaces, and shelters within a limited budget prior to a disaster so that the expected number of casualties is minimized. Under limited budget, Vanajakumari et al (2016) examine an integrated optimization model, which focuses on the problems encountered in the last mile distribution such as determining the locations for temporary warehouse and inventory level along with the number of trucks and the routing of trucks from the warehouse to the point of distribution. Rather than focusing on the last mile, our study departs from Salmerόn and Apte (2010) and Vanajakumari et al (2016) by focusing on the upstream distribution of relief aid, which can be perceived as a macro-level problem for the headquarters of an international HO.…”