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A hybrid hypercube - Genetic algorithm approach for deploying many emergency response mobile units in an urban network

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  • Geroliminis, Nikolas
  • Kepaptsoglou, Konstantinos
  • Karlaftis, Matthew G.
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    Abstract

    Emergency response services are critical for modern societies. This paper presents a model and a heuristic solution for the optimal deployment of many emergency response units in an urban transportation network and an application for transit mobile repair units (TMRU) in the city of Athens, Greece. The model considers the stochastic nature of such services, suggesting that a unit may be already engaged, when an incident occurs. The proposed model integrates a queuing model (the hypercube model), a location model and a metaheuristic optimization algorithm (genetic algorithm) for obtaining appropriate unit locations in a two-step approach. In the first step, the service area is partitioned into sub-areas (called superdistricts) while, in parallel, necessary number of units is determined for each superdistrict. An approximate solution to the symmetric hypercube model with spatially homogeneous demand is developed. A Genetic Algorithm is combined with the approximate hypercube model for obtaining best superdistricts and associated unit numbers. With both of the above requirements defined in step one, the second step proceeds in the optimal deployment of units within each superdistrict.

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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 210 (2011)
    Issue (Month): 2 (April)
    Pages: 287-300

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    Handle: RePEc:eee:ejores:v:210:y:2011:i:2:p:287-300

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    Web page: http://www.elsevier.com/locate/eor

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    Keywords: Emergency response Hypercube Spatial queues Genetic algorithms;

    References

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    Cited by:
    1. Shariat-Mohaymany, Afshin & Babaei, Mohsen & Moadi, Saeed & Amiripour, Sayyed Mahdi, 2012. "Linear upper-bound unavailability set covering models for locating ambulances: Application to Tehran rural roads," European Journal of Operational Research, Elsevier, vol. 221(1), pages 263-272.

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