Given a stochastic project network with independently distributed activity durations, several approaches to bound the distribution function of the project completion time have been proposed. We have implemented the most promising of these algorithms and compare their behavior on a basis of nearly 2000 instances with up to 1200 activities of different test-beds. We propose a suitable numerical representation of the given distributions which is the basis for excellent computational results.

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.

Towards predictable deep-submicron manufacturing

An activity network (AN) is a directed acyclic graph with n nodes and A arcs. The nodes are numbered from 1 to n so that an arc always leads from a smaller numbered node to a higher numbered node. The graph has only one node with no incident arcs, which is called the starting node and numbered 1. Node n is the only node with no emanating arcs and is named the terminal node. An arc represents an activity and a node the start or the culmination of that activity. The terminal node represents the end of the project. These kinds ofgraphsarealsoreferredtoasActivity on Arc (AoA) representation of AN. In DiAN the process represented by the arcs is a diffusion process, the state of which is identified with the remaining work content (rwc). The process starts at time ‘0 ’ at rwc = 1 with a negative drift coefficient. An absorbing barrier is placed at rwc = 0 to identify with the end of the process. The completion time of an activity is thus the first passage time of such a diffusion process. The paradigm of DiAN, while offering an enhanced modeling concept, raises many questions regarding computational challenges, definition of project management metrics and applicability of such a tool in areas beyond project management. The thesis primarily focuses

The problem of determining bounds for application completion times running on generic systems comprised of single or multiple voltagefrequency islands (VFIs) with arbitrary topologies is addressed in the context of manufacturing-driven variability. The approach provides an exact solution for the system-level timing yield in single clock, single voltage (SSV) and VFI systems with an underlying tree-based topology, and a tight upper bound for generic, non-tree based topologies. The results show that: (a) timing yield for overall sourceto-sink completion time for generic systems can be modeled in an exact manner for both SSV and VFI systems; and (b) multiple VFI, latency-constrained systems can achieve 11-90 % higher timing yield than their SSV counterparts. The results are proven formally and supported by experimental results on two embedded applications, namely software defined radio and MPEG2 encoder.