We consider Discrete Event Systems (DES) involving tasks with real-time constraints and seek to control processing times so as to minimize a cost function subject to each task meeting its own constraint. It has been shown that the off-line version of this problem can be efficiently solved by the Critical Task Decomposition Algorithm (CTDA) (Mao et al., IEEE Trans Mobile Comput 6(6): 678-688, 2007). In the on-line version, random task characteristics (e.g., arrival times) are not known in advance. To bypass this difficulty, worst-case analysis may be used. This, however, does not make use of probability distributions and results in an overly conservative solution. In this paper, we develop a new approach which does not rely on worst-case analysis but provides a "best solution in probability" efficiently obtained by estimating the probability distribution of sample-path-optimal solutions. We introduce a condition termed "non-singularity" under which the best solution in probability leads to the on-line optimal control. Numerical examples are included to illustrate our results and show substantial performance improvements over worst- case analysis. © Springer Science + Business Media, LLC 2009.
CITATION STYLE
Mao, J., & Cassandras, C. G. (2010). On-line optimal control of a class of discrete event systems with real-time constraints. Discrete Event Dynamic Systems: Theory and Applications, 20(2), 187–213. https://doi.org/10.1007/s10626-008-0058-z
Mendeley helps you to discover research relevant for your work.