Genetic Algorithm Approach for Casualty Processing Schedule

Date
2022-01-04
Authors
Nistor, Marian Sorin
Pham, Truong Son
Pickl, Stefan
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Abstract
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.
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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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