03:30 PM - 03:55 PM
Reverse procurement auctions: a cost‐effective procurement approach in humanitarian logistics
Establishing agreements with suppliers to ensure quick and cost‐effective procurement of relief supplies is crucial when responding to sudden‐onset disasters. Inspired from practice, we focus on sealed reverse procurement auctions. We propose two two-stage stochastic programming models to support the decision-making, both for the auctioneer (the humanitarian organization) in the announcement phase and for the bidders (commercial suppliers) in the bid construction phase. We present a case study based on a public tender of the Colombian Red Cross, where we generate scenarios based on historical data of disasters in Colombia. Finally, we present insights resulting from numerical experiments.
03:55 PM - 04:20 PM
Collaborative Disaster Preparedness Network Design and Analysis
Prepositioning is a frequently used preparedness strategy to improve response operations. In this study, we design a collaborative preparedness network by considering stock sharing among humanitarian organizations keeping inventory in the same warehouse. To incorporate both pre-disaster and post-disaster decisions under uncertainty, we develop a two-stage stochastic optimization model. We evaluate the benefits of stock sharing based on several performance metrics. Our results show that stock sharing yields a more rapid response and encourages organizations to centralization.
04:20 PM - 04:45 PM
A Two-Stage Stochastic Post-Disaster Humanitarian Relief Network Design Problem
Natural disasters damage infrastructures, break supply chains and force people to relocate. In order to quickly respond to natural disasters, international support (e.g. donations and supplies) is sent to the affected regions. In the absence of a functional distribution network, the design and operation of a Humanitarian Relief Network to receive, store and distribute critical supplies among vulnerable populations is crucial. However, a lack of information regarding the needs assessments of the affected population, damage levels to the infrastructure, and overall effects of possible secondary impacts elevate the level of uncertainty in the region. In this paper, we propose an optimization methodology to solve the Humanitarian Relief Network design problem after a natural disaster. We develop a two-stage stochastic model that explicitly considers uncertainty concerning demand, as well as the transportation and storage capacities of the humanitarian relief network. Given that demand plays a pivotal role in the considered application, we propose a more accurate representation of demand spread, which allows us to model the impacts of unmet demand over time. Finally, numerical results evaluate the proposed model on a data set from the 2018 earthquake in Indonesia.
04:45 PM - 05:10 PM
Mobile Clinic Deployment: A Stochastic Prize Collection Problem
Mobile clinic deployments are often used to provide healthcare to populations in need of humanitarian relief. Practitioners strive to deploy clinics that can access populations with the highest needs. However, during humanitarian operations uncertainty arises in the travel time, usability of roads, and access. We model mobile clinic deployment as a Two Stage Stochastic Prize Collection Problem in an effort to maximize the benefit offered by mobile clinics while considering the uncertainty. Additionally, we study the effect of multiple recourse policies on the mobile clinic deployment plans.