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Genetic Algorithm Optimisation of An Agent-Based Model for Simulating a Retail Market

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  • Alison J Heppenstall
  • Andrew J Evans
  • Mark H Birkin

Abstract

Traditionally, researchers have used elaborate regression models to simulate the retail petrol market. Such models are limited in their ability to model individual behaviour and geographical influences. Heppenstall et al presented a novel agent-based framework for modelling individual petrol stations as agents and integrated important additional system behaviour through the use of established methodologies such as spatial interaction models. The parameters for this model were initially determined by the use of real data analysis and experimentation. This paper explores the parameterisation and verification of the model through data analysis and by use of a genetic algorithm (GA). The results show that a GA can be used to produce not just an optimised match, but results that match those derived by expert analysis through rational exploration. This may suggest that despite the apparent nonlinear and complex nature of the system, there are a limited number of optimal or near optimal behaviours given its constraints, and that both user-driven and GA solutions converge on them.

Suggested Citation

  • Alison J Heppenstall & Andrew J Evans & Mark H Birkin, 2007. "Genetic Algorithm Optimisation of An Agent-Based Model for Simulating a Retail Market," Environment and Planning B, , vol. 34(6), pages 1051-1070, December.
    • Handle: RePEc:sae:envirb:v:34:y:2007:i:6:p:1051-1070
    DOI: 10.1068/b32068
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    References listed on IDEAS

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    1. Galeotti, Marzio & Lanza, Alessandro & Manera, Matteo, 2003. "Rockets and feathers revisited: an international comparison on European gasoline markets," Energy Economics, Elsevier, vol. 25(2), pages 175-190, March.
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    Cited by:

    1. He, Zhou & Cheng, T.C.E. & Dong, Jichang & Wang, Shouyang, 2016. "Evolutionary location and pricing strategies for service merchants in competitive O2O markets," European Journal of Operational Research, Elsevier, vol. 254(2), pages 595-609.

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