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A Genetic Algorithm for Optimizing Mobile Stroke Unit Deployment
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0003-2769-4826
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).ORCID-id: 0000-0002-8209-0921
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
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2023 (engelsk)Inngår i: Procedia Computer Science, ISSN 1877-0509, Vol. 225, s. 3536-3545Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Elsevier, 2023. Vol. 225, s. 3536-3545
Emneord [en]
genetic algorithm, mobile stroke unit (MSU), optimization, healthcare, time to treatment
HSV kategori
Identifikatorer
URN: urn:nbn:se:mau:diva-64632DOI: 10.1016/j.procs.2023.10.349OAI: oai:DiVA.org:mau-64632DiVA, id: diva2:1821342
Konferanse
27th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES 2023), Athens, Greece, 6-8 September 2023
Forskningsfinansiär
The Kamprad Family FoundationTilgjengelig fra: 2023-12-20 Laget: 2023-12-20 Sist oppdatert: 2023-12-20bibliografisk kontrollert

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

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