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A Genetic Algorithm for Optimizing Mobile Stroke Unit Deployment
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0003-2769-4826
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-8209-0921
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
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2023 (English)In: Procedia Computer Science, ISSN 1877-0509, Vol. 225, p. 3536-3545Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 225, p. 3536-3545
Keywords [en]
genetic algorithm, mobile stroke unit (MSU), optimization, healthcare, time to treatment
National Category
Communication Systems Neurology
Identifiers
URN: urn:nbn:se:mau:diva-64632DOI: 10.1016/j.procs.2023.10.349OAI: oai:DiVA.org:mau-64632DiVA, id: diva2:1821342
Conference
27th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES 2023), Athens, Greece, 6-8 September 2023
Funder
The Kamprad Family FoundationAvailable from: 2023-12-20 Created: 2023-12-20 Last updated: 2023-12-20Bibliographically approved

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Abid, Muhammad AdilAmouzad Mahdiraji, SaeidLorig, FabianHolmgren, JohanMihailescu, Radu-Casian

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Abid, Muhammad AdilAmouzad Mahdiraji, SaeidLorig, FabianHolmgren, JohanMihailescu, Radu-Casian
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