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A Genetic Algorithm for Scheduling Laboratory Rooms: A Case Study

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Applied Informatics (ICAI 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1643))

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Abstract

Genetic algorithms (GAs) are a great tool for solving optimization problems. Their characteristics and different components based on the principles of biological evolution make these algorithms very robust and efficient in this type of problem. Many research works have presented dedicated solutions to schedule or resource optimization problems in different areas and project types; most of them have adopted GA implementation to find an individual that represents the best solution. Under this conception, in this work, we present a GA with a controlled mutation operator aiming at maintaining a trade-off between diversity and survival of the best individuals of each generation. This modification is supported by an improvement in terms of convergence time, efficiency of the results and the fulfillment of the constraints (of 29%, 14.98% and 23.33% respectively, compared with state-of-the-art GA with a single random mutation operator) to solve the problem of schedule optimization in the use of three laboratory rooms of the Mechatronics Engineering Career of the International University of Ecuador.

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Acknowledgments

This work is supported by SDAS Research Group (https://sdas-group.com).

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Authors and Affiliations

  1. Department of Mechatronics, Universidad Internacional Del Ecuador, Av. Simon Bolivar, 170411, Quito, Ecuador

    Rafael Fuenmayor, Martín Larrea, Mario Moncayo, Esteban Moya, Sebastián Trujillo, Juan-Diego Terneus, Robinson Guachi & Lorena Guachi-Guachi

  2. Modeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid Ben Guerir, 43150, Ben Guerir, Morocco

    Diego H. Peluffo-Ordoñez

  3. Smart Data Analysis Systems Group (SDAS Research Group), Ben Guerir, 47963, Morocco

    Diego H. Peluffo-Ordoñez & Lorena Guachi-Guachi

  4. Faculty of Engineering, Corporación Universitaria Autónoma de Nariño, Pasto, 520001, Colombia

    Diego H. Peluffo-Ordoñez

Authors
  1. Rafael Fuenmayor
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  2. Martín Larrea
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  5. Sebastián Trujillo
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  6. Juan-Diego Terneus
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  7. Robinson Guachi
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  8. Diego H. Peluffo-Ordoñez
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  9. Lorena Guachi-Guachi
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Corresponding author

Correspondence to Lorena Guachi-Guachi .

Editor information

Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia

    Hector Florez

  2. Universidad Continental, Arequipa, Peru

    Henry Gomez

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Fuenmayor, R. et al. (2022). A Genetic Algorithm for Scheduling Laboratory Rooms: A Case Study. In: Florez, H., Gomez, H. (eds) Applied Informatics. ICAI 2022. Communications in Computer and Information Science, vol 1643. Springer, Cham. https://doi.org/10.1007/978-3-031-19647-8_1

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  • DOI: https://doi.org/10.1007/978-3-031-19647-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19646-1

  • Online ISBN: 978-3-031-19647-8

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