A Genetic Algorithm for Optimizing Mobile Stroke Unit Deployment
(2023) 27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023 In Procedia Computer Science 225. p.3536-3545- Abstract
A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem.... (More)
A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method's flexibility and adaptability through a series of experiments across multiple settings. For the considered scenario, our proposed method outperforms the exhaustive search method by finding the best locations within 0.16, 1.44, and 10.09 minutes in the deployment of three MSUs, four MSUs, and five MSUs, resulting in 8.75x, 16.36x, and 24.77x faster performance, respectively. Furthermore, we validate the method's robustness by iterating GA multiple times and reporting its average fitness score (performance convergence). In addition, we show the effectiveness of our method by evaluating key hyperparameters, that is, population size, mutation rate, and the number of generations.
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- author
- Abid, Muhammad Adil ; Mahdiraji, Saeid Amouzad ; Lorig, Fabian ; Holmgren, Johan ; Mihailescu, Radu Casian and Petersson, Jesper LU
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- genetic algorithm, healthcare, mobile stroke unit (MSU), optimization, time to treatment
- host publication
- Procedia Computer Science
- series title
- Procedia Computer Science
- volume
- 225
- pages
- 10 pages
- conference name
- 27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023
- conference location
- Athens, Greece
- conference dates
- 2023-09-06 - 2023-09-08
- external identifiers
-
- scopus:85183561235
- ISSN
- 1877-0509
- DOI
- 10.1016/j.procs.2023.10.349
- language
- English
- LU publication?
- yes
- id
- 65975035-dea7-4daa-b0d8-98015dac50cb
- date added to LUP
- 2024-02-15 15:07:58
- date last changed
- 2024-02-15 15:07:58
@inproceedings{65975035-dea7-4daa-b0d8-98015dac50cb,
abstract = {{<p>A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method's flexibility and adaptability through a series of experiments across multiple settings. For the considered scenario, our proposed method outperforms the exhaustive search method by finding the best locations within 0.16, 1.44, and 10.09 minutes in the deployment of three MSUs, four MSUs, and five MSUs, resulting in 8.75x, 16.36x, and 24.77x faster performance, respectively. Furthermore, we validate the method's robustness by iterating GA multiple times and reporting its average fitness score (performance convergence). In addition, we show the effectiveness of our method by evaluating key hyperparameters, that is, population size, mutation rate, and the number of generations.</p>}},
author = {{Abid, Muhammad Adil and Mahdiraji, Saeid Amouzad and Lorig, Fabian and Holmgren, Johan and Mihailescu, Radu Casian and Petersson, Jesper}},
booktitle = {{Procedia Computer Science}},
issn = {{1877-0509}},
keywords = {{genetic algorithm; healthcare; mobile stroke unit (MSU); optimization; time to treatment}},
language = {{eng}},
pages = {{3536--3545}},
series = {{Procedia Computer Science}},
title = {{A Genetic Algorithm for Optimizing Mobile Stroke Unit Deployment}},
url = {{http://dx.doi.org/10.1016/j.procs.2023.10.349}},
doi = {{10.1016/j.procs.2023.10.349}},
volume = {{225}},
year = {{2023}},
}