Strategic fire and rescue service decision making using evolutionary algorithms

Abstract

This paper describes the development of a novel, risk based method to locate high performance solutions for the deployment of Fire and Rescue Service (FRS) resources, such as fire stations and appliances, using evolutionary algorithms in conjunction with Fire Service Cover Models. Such algorithms allow the relatively rapid identification of areas of good potential solutions by sampling only a small percentage of the total search space. A real example of the use of the software to optimise vehicle locations is presented which identifies significant potential increase in efficiency and effectiveness over the existing vehicle locations