A heuristic for optimizing stochastic activity networks with applications to statistical digital circuit sizing (2004)
| Venue: | IEEE Transactions on Circuits and Systems-I |
| Citations: | 8 - 4 self |
BibTeX
@TECHREPORT{Kim04aheuristic,
author = {Seung-jean Kim and Stephen P. Boyd and Sunghee Yun and Dinesh D. Patil and Mark A. Horowitz},
title = {A heuristic for optimizing stochastic activity networks with applications to statistical digital circuit sizing},
institution = {IEEE Transactions on Circuits and Systems-I},
year = {2004}
}
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Abstract
A deterministic activity network (DAN) is a collection of activities, each with some duration, along with a set of precedence constraints, which specify that activities begin only when certain others have finished. One critical performance measure for an activity network is its makespan, which is the minimum time required to complete all activities. In a stochastic activity network (SAN), the durations of the activities and the makespan are random variables. The analysis of SANs is quite involved, but can be carried out numerically by Monte Carlo analysis. This paper concerns the optimization of a SAN, i.e., the choice of some design variables that affect the probability distributions of the activity durations. We concentrate on the problem of minimizing a quantile (e.g., 95%) of the makespan, subject to constraints on the variables. This problem has many applications, ranging from project management to digital integrated circuit (IC) sizing (the latter being our motivation). While there are effective methods for optimizing DANs, the SAN optimization problem is much more difficult; the few existing methods cannot handle large-scale problems.
