A modified grouping genetic algorithm to select ambulance site locations

Alexis J Comber,Satoshi Sasaki,Hiroshi Suzuki,Chris Brunsdon

doi:10.1080/13658816.2010.501334

Abstract

This article describes the development and application of a modified grouping genetic algorithm (GGA) used to identify sets of optimal ambulance locations. The GGA was modified to consider a special case with only two groups, and the reproduction and mutation schemes were modified to operate more efficiently. It was applied to a case study locating ambulances from a fixed set of alternative locations. The sites were evaluated using data of emergency medical services (EMS) calls summarised over census areas and weighted by network distance. Census areas serviced by the same selected location defined ambulance catchments. The results indicated alternative sites for ambulances to be located, with average EMS response times improved by 1 min 14 s, and showed the impacts of having different numbers of ambulances in current locations and in new locations. The algorithmic developments associated with the modified GGA and the advantages of using census areas as spatial units to summarise data are discussed.

Highlights

Introduction to GAs and Grouping Genetic Algorithm (GGA)The optimisation process in GA proceeds as follows
The modified GGA was run to select 27 ambulances site locations evaluated on the network distance between each census centroids, weighted by the count of emergency medical services (EMS) cases
That 23 out the current 27 locations were selected by the GGA is an interesting result: it indicates some degree of optimality in the choice of current ambulance locations amongst the 35 fire stations

Summary

Introduction

Introduction to GAs and GGAsThe optimisation process in GA proceeds as follows. A potential set of solutions is created, sometimes called a ‘string’ (Huang et al, 2004) or more usually a ‘chromosome’. Genes from successful individuals (i.e. passing the criteria) are interchanged between individuals This is done in a crossover which combines two chromosomes to create new chromosomes, which in turn form new individuals, in effect offspring or children. Mutation occasionally adds some random new genes into the chromosome In this way the GA ‘breeds’ optimal solutions by creating a more optimised (fitter) generation and the analogy with natural selection: crossover creates new chromosomes from successful ones, and mutation ensures diversity. Models that incorporate GAs are frequently referred to as ‘optimisation models’ in the literature They simulate the process of genetic mutation, gene combination, and selection in biological evolution. A set of simple scenarios illustrating this can be found in the genalg R package (Willighagen, 2005)

Methods

Results

Discussion

Conclusion