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The Multirate Resource Constraint

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9892))

Abstract

Many real world cyclic scheduling problems involve applications that need to be repeated with different periodicity. For example, multirate control systems present multiple control loops that are organized hierarchically: the higher-level loop responds to the slower system dynamics and typically its period can be a few orders of magnitude longer than the lowest level. Cyclic scheduling problems can be cast into classical RCPSP instances via a technique called unfolding [4, 6], which causes graph expansion. In the case of multirate applications, this expansion can be significantly large. In this context, finding a high-quality allocation and schedule could be very challenging. In this paper, we propose a new Multirate Resource Constraint, modeling unary resources, that avoids graph expansion by exploiting the multirate nature of the schedule in its filtering algorithm. In an experimentation on synthetic and real-world instances, we show that our method drastically outperforms approaches based on state-of-the-art unfolding and constraint based scheduling.

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Notes

  1. 1.

    In the whole paper we assume that the durations and the periods are positive values.

  2. 2.

    The generator and the synthetic instances solved in this work can be found at https://github.com/alessioBonfietti/Multirate-Resource-Constraint-Repo.

  3. 3.

    The time limit value refers to the limit we use for the subproblem of the last application (the hardest). The previous subproblems were solved with a time limit halved at each step (e.g. if we have three applications, the limits would be 75,150,300, respectively).

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Acknowledgements

We would like to show our gratitude to William Aeby for his assistance in extracting the industrial instances.

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Correspondence to Alessio Bonfietti .

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Bonfietti, A., Zanarini, A., Lombardi, M., Milano, M. (2016). The Multirate Resource Constraint. In: Rueher, M. (eds) Principles and Practice of Constraint Programming. CP 2016. Lecture Notes in Computer Science(), vol 9892. Springer, Cham. https://doi.org/10.1007/978-3-319-44953-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-44953-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44952-4

  • Online ISBN: 978-3-319-44953-1

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