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Optimal Deployment in Emergency Medicine with Genetic Algorithm Exemplified by Lifeguard Assignments

Jonas Chromik, Bert Arnrich

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed

    In emergency medicine, workforce planning needs to satisfy a number of constraints. There are hard constraints regarding qualifications and soft constraints regarding the wishes of the personnel. One instance of such a planning problem is the assignment of lifeguards at the coasts of the North Sea and the Baltic Sea in Germany. These lifeguards are volunteers and thus accounting for wishes is crucial while qualification constraints must be satisfied nevertheless. This paper presents a genetic algorithm that solves this problem with sub-second runtime. We compare this genetic algorithm to a brute force solution creating optimal solutions at the expense of larger runtime complexity. The genetic approach outperforms the brute force approach in terms of runtime when there are more than 3 places of deployment while consistently producing optimal solutions within less than 10 generations.

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