Genetic Algorithm Approach for Casualty Processing Schedule

dc.contributor.author Nistor, Marian Sorin
dc.contributor.author Pham, Truong Son
dc.contributor.author Pickl, Stefan
dc.date.accessioned 2021-12-24T17:28:48Z
dc.date.available 2021-12-24T17:28:48Z
dc.date.issued 2022-01-04
dc.description.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.
dc.format.extent 9 pages
dc.identifier.doi 10.24251/HICSS.2022.167
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/79499
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Decision Analytics, Machine Learning, and Field Experimentation for Defense and Emergency Response
dc.subject mass causality incident response
dc.subject processing casualty schedule
dc.subject genetic algorithm
dc.subject flexible job-shop scheduling problem.
dc.title Genetic Algorithm Approach for Casualty Processing Schedule
dc.type.dcmi text
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
0134.pdf
Size:
566.67 KB
Format:
Adobe Portable Document Format
Description: