Assesing demand in stochastic locational planning problems: An Artificial Intelligence approach for emergency service systems

Yorgos Photis () and George Grekoussis

MPRA Paper from University Library of Munich, Germany

Abstract: The efficiency of emergency service systems is measured in terms of their ability to deploy units and personnel in a timely and effective manner upon an event’s occurrence. Aiming to exploit stochastic demand, spatial tracing and location analysis of emergency incidents are examined through the utilisation of Artificial Intelligence in two interacting levels. Firstly, spatio-temporal point pattern of demand is analysed by a new genetic algorithm. The proposed genetic algorithm interrelates sequential events formulating moving events and as a result, every demand point pattern is correlated both to previous and following events. Secondly, the approach provides the ability to predict, by means of neural networks optimised by genetic algorithms, how the pattern of demand will evolve and thus location of supplying centres and/or vehicles can be optimally defined. Neural networks provide the basis for a spatio-temporal clustering of demand, definition of the relevant centres, formulation of possible future states of the system and finally, definition of locational strategies for the improvement of the provided services.

Keywords: Locational planning; Point Pattern Analysis; Spatial analysis; Artificial Intelligence (search for similar items in EconPapers)
JEL-codes: C45 C61 R53 (search for similar items in EconPapers)
Date: 2003
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Published in Conference Proceedings of the 2005 Conference on Computers in Urban Planning and Urban Management (CUPUM 05) 05.373(2003): pp. 1-16

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:20678

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