Production and Maintenance Scheduling Supported by Genetic Algorithms

  • Duarte AlemãoEmail author
  • Mafalda Parreira-Rocha
  • José Barata
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 530)


The market demand has changed in recent years due to increased interest in more customized and diversified products by the consumers, leading to a change in production lines, which are becoming more flexible and dynamic. At the same time, the amount of data available in the factories is growing more and more, thereby the number of errors in the production schedule may occur often. Several approaches have been used over time to plan and schedule the shop-floor production. However, some only consider static environments, where the tasks are allocated to the machines, not considering that machines may not be available and sometimes maintenance interventions are needed. The introduction of maintenance increases the scheduling complexity and makes it harder to allocate the tasks efficiently. So, new solutions have been proposed, giving manufacturing systems the ability to quickly adapt to some disturbances that may occur. Thus, Artificial Intelligence approaches have been adopted to do the task allocation for the shop-floor. Those approaches can find suitable solutions faster than traditional approaches. This article proposes an architecture, based on Genetic Algorithm, capable of generating schedules including both production and maintenance tasks.


Dynamic job-shop scheduling Maintenance task allocation Genetic algorithms Manufacturing systems 


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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Duarte Alemão
    • 1
    Email author
  • Mafalda Parreira-Rocha
    • 1
  • José Barata
    • 1
  1. 1.UNINOVA, CTS, Faculdade de Ciências e TecnologiasNOVA University of LisbonCaparicaPortugal

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