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

Photis, Yorgos N. and Grekoussis, George (2003): Assesing demand in stochastic locational planning problems: An Artificial Intelligence approach for emergency service systems. Published in: Conference Proceedings of the 2005 Conference on Computers in Urban Planning and Urban Management (CUPUM 05) , Vol. 373, No. 05 (2003): pp. 1-16.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Locational planning; Point Pattern Analysis; Spatial analysis; Artificial Intelligence
• Subjects: R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R5 - Regional Government Analysis > R53 - Public Facility Location Analysis ; Public Investment and Capital Stock; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis

References

Alp O., Erkut E., Drenzer Z.(2003) An efficient genetic algorithm for the p-median problem, Annals of Operational Research 122, 21-42, Kluwer Academic Publishers

Berman O.,and Parkan C. (1981) A facility location problem with distance dependent demand Decision Sciences, 12: 653-632.

Beckmann M., (1999), Lectures on location theory, Springer – Verlag.

Daskin M., (1995), Network and Discrete Location, J.Wiley & Suns, N.Y

Fischer M., and Leung Y., (1998) A genetic-algorithms based evolutionary computational neural network for modeling spatial interaction data. Annals of Regional Science 32:437–458

Goldberg D.,E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Pub Co Labbe M., and Hakimi S.L.,(1990) Market and locational equilibrium for two competitors, Operational Research 39: 749-756.

Laporate G., Louneaux F.V.,and Van Hamme L.(1994) Exact solution of a stochastic location by an integer L-shaped algorithm. Transportation Science, 28:95-103.

Lederer P.J., and Thisse J.F., (1990) Competitive location on network under delivered pricing, Operational Research. Lett., 9:147-153.

Lu Y., Thill J-C. (2003) Assessing the cluster correspondence between paired point locations, Geographical Analysis, Vol35, No4. The Ohio State University.

Perl J., and Ho P.K., (1990) Public facilities location under elastic demand, Transportation Science,24:117-136.

Openshaw S., and C.Openshaw (1997), Artificial Intelligence in Geography. John Wiley & Sons Ltd. England.

Owen S.H, Daskin M.S. (1998), Strategic facility location: A review, European Journal of Operational Research 111. pp 423-447.

O’Sullivan, D., and D. Unwin (2003) Geographic Information Analysis. Hoboken, NJ: John Wiley & Sons.

Wall M., (1996) GAlib: A C++ Library of Genetic Algorithm Components version 2.4 Documentation Revision B. Massachusetts Institute of Technology

Wanger J.L, and Falkson L.M., (1975) The optimal nodal location of public facilities with price-sensitive demand, Geographical Analysis, 7: 69-83.