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A Mixed-Integer Linear Programming Model and a Simulated Annealing Algorithm for the Long-Term Preventive Maintenance Scheduling Problem

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Intelligent Systems Design and Applications (ISDA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 736))

Abstract

This paper addresses a problem arising in the long-term maintenance programming of an iron ore processing plant of a company in Brazil. The problem is a complex maintenance programming where we have to assign the equipment preventive programming orders to the available work teams over a 52 week planning. We first developed a general mixed integer programming model which was not able for solving real instances using the CPLEX optimizer. Therefore, we also proposed a heuristic approach, based on the Simulated Annealing meta-heuristic, that was able to handle the instances.

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Acknowledgements

The authors thank FAPEMIG, CNPq and UFOP for supporting this research.

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Authors and Affiliations

  1. Departamento de Computação, Universidade Federal de Ouro Preto, Ouro Preto, Brazil

    Roberto D. Aquino, Jonatas B. C. Chagas & Marcone J. F. Souza

Authors
  1. Roberto D. Aquino
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  2. Jonatas B. C. Chagas
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  3. Marcone J. F. Souza
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Corresponding author

Correspondence to Roberto D. Aquino .

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Editors and Affiliations

  1. Machine Intelligence Research Labs , Auburn, Washington, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka , Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs , Auburn, Washington, USA

    Niketa Gandhi

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Aquino, R.D., Chagas, J.B.C., Souza, M.J.F. (2018). A Mixed-Integer Linear Programming Model and a Simulated Annealing Algorithm for the Long-Term Preventive Maintenance Scheduling Problem. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_15

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  • DOI: https://doi.org/10.1007/978-3-319-76348-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76347-7

  • Online ISBN: 978-3-319-76348-4

  • eBook Packages: EngineeringEngineering (R0)

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