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.

The multirate resource constraint

BONFIETTI, ALESSIO;ZANARINI, ALESSANDRO;LOMBARDI, MICHELE;MILANO, MICHELA
2016

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.
2016
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
113
129
Bonfietti, Alessio; Zanarini, Alessandro; Lombardi, Michele; Milano, Michela
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/571265
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