15h30 - 15h55
A collaborative prepositioning model for strengthening local disaster response capacity
We present a prepositioning strategy and a stochastic optimization model to strengthen disaster preparedness of a region, which involves multiple countries prone to disaster risk, such as the Caribbean. We consider the uncertainties related to the location and impact of disasters, and present a collaborative approach based on risk-pooling.
15h55 - 16h20
Food aid modality selection problem
There is a vast empirical literature suggesting that providing food aid in cash or vouchers is significantly more effective compared to in-kind. Yet, ours is the first study that mathematically models the aid modality selection and provides a methodology that can respond the dynamics of the environments requiring food assistance.
16h20 - 16h45
A robust optimization approach for humanitarian needs assessment planning under travel time uncertainty
In this study, we focus on rapid needs assessment operations conducted immediately after a disaster to investigate the conditions of different affected community groups, and address the problem of selecting the sites to be visited by the assessment teams during a fixed assessment period and constructing assessment routes. We represent uncertain travel times by specifying a range of values, and propose a robust optimization approach to support site selection and routing decisions. We present tractable formulations that are robust with respect to different uncertainty sets and propose using the robust formulation with a co-axial box uncertainty set for rapid assessment planning. We develop an efficient tabu search heuristic to solve the proposed model. We present computational results to test our solution method and illustrate our approach on a case study, which is based on data from Van (a southeastern province of Turkey) earthquake in October 2011.
16h45 - 17h10
Risk evaluations of transportation corridors for humanitarian aid: The case of the World Food Programme based in Niger
We evaluate the risks affecting the World Food Programme's logistic supply chain, upstream of its food distribution to the beneficiaries in Niger. We are developing a descriptive and predictive model able to evaluate the decision taken under uncertainty. It is driven by the intent of generating valid scenarios that quantifies the stochastic parameters of the case studied: the delays, the cost and the losses.