Stochastic network models for logistics planning in disaster relief

Abstract

Emergency logistics in disasters is fraught with planning and operational challenges, such as uncertainty about the exact nature and magnitude of the disaster, a lack of reliable information about the location and needs of victims, possible random supplies and donations, precarious transport links, scarcity of resources, and so on. This paper develops a new two-stage stochastic network flow model to help decide how to rapidly supply humanitarian aid to victims of a disaster within this context. The model takes into account practical characteristics that have been neglected by the literature so far, such as budget allocation, fleet sizing of multiple types of vehicles, procurement, and varying lead times over a dynamic multiperiod horizon. Attempting to improve demand fulfillment policy, we present some extensions of the model via state-of-art risk measures, such as semideviation and conditional value-at-risk. A simple two-phase heuristic to solve the problem within a reasonable amount of computing time is also suggested.Numerical tests based on the floods and landslides in Rio de Janeiro state, Brazil, show that the model can help plan and organise relief to provide good service levels in most scenarios, and how this depends on the type of disaster and resources. Moreover, we demonstrate that our heuristic performs well for real and random instances.

Emergency logistics for wildfire suppression based on forecasted disaster evolution

“…Huang and Song (2016) discussed an emergency logistics distribution routing problem based on uncertainty theory in which some parameters lack historical data and are given by experts' estimations. Alem et al (2016) developed a new two-stage stochastic network flow model to decide how to rapidly supply humanitarian aid to disaster victims considering the uncertainty about the exact nature and magnitude of the disaster.…”

Heuristic approaches for the multiperiod location-transportation problem with reuse of vehicles in emergency logistics

“…Relief centers were classified similarly as in the study of Rawls and Turnquist (2010) as small, medium, and large. The characteristics and costs of the emergency products and vehicles were based on the existing literature (IBGE, 2009;The Sphere Project, 2011;ICRC, 2009;Alem and Clark, 2014). The shipping costs γ ijl were considered proportional to the fuel consumption and to the distance between depots and relief centers.…”

Disaster preparedness in humanitarian logistics: A collaborative approach for resource management in floods

“…The model designed contains only deterministic variables. Liberatore, Pizarro, de Blas, Ortuño, and Vitoriano (2013) and Alem, Clark, and Moreno (2016) identified the main sources of uncertainty on emergency logistics: demand, demand location, affected areas, supply, and transportation network.…”