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Evolutionary multiobjective optimization for emergency medical services

Authors:

Javier López,

Laura Lanzarini,

Armando De GiustiAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Pages 83 - 84

https://doi.org/10.1145/2001858.2001907

Published: 12 July 2011 Publication History

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Abstract

In this paper, the use of evolutionary metaheuristics for the optimization of emergency medical services (EMS) applied to a real-world case in Argentina is analyzed. The problem requires the simultaneous optimization of two opposing objectives -- reducing service delay time and minimizing the use of third-party medical vehicle. Therefore, a multiobjective technique was implemented. Several multiobjective techniques that had good results reported in the literature were assessed. The techniques that presented the best indicators in this case were selected. Also, a disturbance operator that improves the results found by the assessed algorithms was developed. The objectives were achieved. A process to dispatch medical vehicles to home medical services based on evolutionary computing was successfully carried out, maximizing the use of the available installed capacity, improving response time rates and using a smaller amount of resources.

References

[1]

Dantzig, G. and Ramser, J., The Truck Dispatching Problem. Management Science,6:80--91, 1959.

Digital Library

Google Scholar

[2]

Deb, K. Pratap, A.Agarwal, S. and Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 182--197, 2002.

Digital Library

Google Scholar

[3]

Knowles, J. D., and Corne, D. W., The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisation. (CEC99), volume 1, pages 98--105, Piscataway, NJ, IEEE Press. 1999.

Google Scholar

[4]

López, J., Lanzarini, L.,De Giusti, A., VarMOPSO: Multi-Objective Particle Swarm Optimization with Variable Population Size, Advances in Artificial Intelligence -- IBERAMIA 2010, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, pp 60--69. 2010.

Digital Library

Google Scholar

[5]

Nebro, A., Durillo, J., García-Nieto, Coello Coello, C., Luna F., and Alba, E., SMPSO: A New PSO- based Metaheuristic for Multi-objective Optimization. IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MCDM 2009), pp: 66--73. March 2009.

Crossref

Google Scholar

[6]

Osyczka, A., Multicriteria optimization for engineering design, In : Design Optimization, Academic Press pp. 193--227. 1985.

Google Scholar

[7]

Reyes Sierra, M. and Coello Coello, C. Improving PSO-Based Multiobjective Optimization Using Crowding, Mutation and ë-Dominance. In Evolutionary Multi-Criterion Optimization, LNCS 3410, pages 505--519, 2005.

Crossref

Google Scholar

[8]

Zitzler, E. Laumanns, M. and Thiele, L., SPEA2: Improving the Strength Pareto Evolutionary Algorithm. In K. Giannakoglou et al., editor, EUROGEN 2001, pages 95--100, Athens, Greece, 2002.

Google Scholar

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Tedesco DFeletti GTrucco P(2024)Hospital selection decision in emergency medical services: a simulation-based assessment of assignment criteriaJournal of Industrial and Production Engineering10.1080/21681015.2024.2348557(1-19)Online publication date: 3-May-2024
https://doi.org/10.1080/21681015.2024.2348557
Tedesco DFeletti GTrucco P(2023)Hospital Selection in Emergency Medical Services: A Discrete Event Simulation Approach to Test Different PoliciesProceedings of the 2023 10th International Conference on Industrial Engineering and Applications10.1145/3587889.3587893(19-27)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1145/3587889.3587893

Index Terms

Evolutionary multiobjective optimization for emergency medical services
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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Information

Published In

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

July 2011

1548 pages

ISBN:9781450306904

DOI:10.1145/2001858

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Tedesco DFeletti GTrucco P(2024)Hospital selection decision in emergency medical services: a simulation-based assessment of assignment criteriaJournal of Industrial and Production Engineering10.1080/21681015.2024.2348557(1-19)Online publication date: 3-May-2024
https://doi.org/10.1080/21681015.2024.2348557
Tedesco DFeletti GTrucco P(2023)Hospital Selection in Emergency Medical Services: A Discrete Event Simulation Approach to Test Different PoliciesProceedings of the 2023 10th International Conference on Industrial Engineering and Applications10.1145/3587889.3587893(19-27)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1145/3587889.3587893

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Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: The extended NOSGA method