Planning for Hurricane Isaac using Probability Theory in a Linear Programming Model

Abstract

The author demonstrated how linear programming (LP) models with embedded probability theory were applied for disaster planning to mitigate the damages of hurricane Isaac. The purpose of the article was to raise awareness of software-based disaster planning methods, and to demonstrate how uncertainty can be quantified as risk estimates to substitute for and then added as constraints in LP models. Several LP approaches and alternatives were reviewed from the literature. Three LP problem-solving techniques were demonstrated: graphing, algebraic systems of linear equations, and using spreadsheet software. Two disaster planning LP models were solved based on the Federal Emergency Management Agency case study of hurricane Isaac in 2012. The case study focused on allocating emergency supplies to strategic Point of Distribution locations. A unique feature of the article was showing how uncertainty could be quantified as risk by calculating the mean, standard deviation and coefficient of variation for airboat trips based on historical data from hurricane Katrina. Several insights of LP model formulation were given to assist others.