Abstract
Bender et al. (SPAA 2013) proposed a theoretical framework for testing in contexts where safety mistakes must be avoided. Testing in such a context is made by machines that need to be often calibrated. Since calibrations have non negligible cost, it is important to study policies minimizing the calibration cost while performing all the necessary tests. We focus on the single-machine setting and we study the complexity status of different variants of the problem. First, we extend the model by considering that the jobs have arbitrary processing times and that the preemption of jobs is allowed. For this case, we propose an optimal polynomial time algorithm. Then, we study the case where there is many types of calibrations with different lengths and costs. We prove that the problem becomes NP-hard for arbitrary processing times even when the preemption of the jobs is allowed. Finally, we focus on the case of unit-time jobs and we show that a more general problem, where the recalibration of the machine is not instantaneous, can be solved in polynomial time.
This work has been supported by the ALGONOW project of the THALES program and the Special Account for Research Grants of National and Kapodistrian University of Athens, by NSFC (Nos. 61433012, U1435215), and by Shenzhen basic research grant JCYJ20160229195940462.
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Notes
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An edf policy is a schedule in which at any time, the job with the smallest deadline among the available jobs is scheduled first.
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Angel, E., Bampis, E., Chau, V., Zissimopoulos, V. (2017). On the Complexity of Minimizing the Total Calibration Cost. In: Xiao, M., Rosamond, F. (eds) Frontiers in Algorithmics. FAW 2017. Lecture Notes in Computer Science(), vol 10336. Springer, Cham. https://doi.org/10.1007/978-3-319-59605-1_1
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DOI: https://doi.org/10.1007/978-3-319-59605-1_1
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