We consider a stochastic pre-disaster relief network design problem, which mainly determines the capacities and locations of the response facilities and their inventory levels of the relief supplies in the presence of uncertainty in post-disaster demands and transportation network conditions.
For instance, a recent study, utilizing a comprehensive data set encompassing 187 international political entities from 1950 to the present, provided robust evidence that stochastic natural disasters significantly increased the risk of violent civic conflicting in the short term and medium term (Nel and Righarts 2008).
Stochastic disturbances, such as natural disasters (e.g., earthquakes, volcanic eruptions, tsunamis, hurricanes, epidemics, floods) and human-caused catastrophes (e.g., toxic spills, nuclear accidents, plane crashes), have been shown to significantly disrupt social order (increased incidences of looting, physical abuse, divorce, etc).
Each trial of the six experiments had random parameter values, and a logic component for how other agencies in the disaster environment acted with stochastic perturbation.
A stochastic optimization in the preparedness phase of disaster management was proposed to determine the storage locations of medical supplies and requested inventory amounts for each type of medical supply.
Mean annual increment and its standard deviation of the mean annual increment are therefore calculated and reported for all stochastic runs, as well as the number of fire disasters and the mean age at which the disasters occurred.
This paper studies sheltering network planning and operations for natural disaster preparedness and responses with a two-stage stochastic program.
This work develops a multi-objective, two-stage stochastic, non-linear, and mixed-integer mathematical model for relief pre-positioning in disaster management.
Recently, due to the need to investigate the impacts of natural disasters, as well as technology development, these tools were enhanced with stochastic programming and mixed-integer programming techniques [11].
In this study, we propose a multi-objective stochastic programming model for developing an earthquake response plan, which integrates pre-and post-disaster decisions.
