Genetic Algorithm Approach for Casualty Processing Schedule

Nistor, Marian Sorin
Pham, Truong Son
Pickl, Stefan
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Searching for an optimal casualty processing schedule can be considered a key element in the MCI response phase. Genetic algorithm (GA) has been widely applied for solving this problem. In this paper, it is proposed a GA-based optimization model for addressing the casualty processing scheduling problem (CPSP). It aims to develop a GA-based optimization model in which only a part of the chromosome (solution) involves in the evolutionary process. This can result in a less complex training process than previous GA-based approaches. Moreover, the study attempts to investigate two common objectives in CPSP: maximizing the number of survivals and minimizing the makespan. The proposed GA-based model is evaluated on two real-world scenarios in the Republic of Moldova, FIRE, and FLOOD. The paper suggests that GA models with a population size of 500 or smaller can be applied for MCI scenarios. The first objective can help many casualties receiving specialization treatments at hospitals.
Decision Analytics, Machine Learning, and Field Experimentation for Defense and Emergency Response, mass causality incident response, processing casualty schedule, genetic algorithm, flexible job-shop scheduling problem.
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