Forschungs- & Entwicklungsinformationsdienst der Gemeinschaft - CORDIS

Modelling frameworks for scheduling under (discrete) uncertainty

At the beginning of the project, one class of problems of scheduling under uncertainty was explored, where the uncertainty is associated with task durations. For this problem interesting results that propose an optimality criterion more refined than simple worst-case performance.

A scheduler synthesis algorithm was implemented for this class of problems and the schedules it generated on simple examples turned out to be much more adaptive to the real duration of tasks than other types of solutions.

In the second and third year the complementary problem of scheduling under discrete uncertainty has been tackled. It covers the situation where the choice of tasks that need to be executed may depend on the results of other tasks, results that become known only after the termination of these tasks.

Such situations are very common in scheduling of real time programs, where the results correspond to testing conditions inside if statements, but it can also be found in manufacturing, for example when certain production steps may terminate successfully or fail.

A modeling framework for this problem using conditional dependency graphs, transformed into timed automata with discrete adversaries have been developed. Several exact and heuristic algorithms for synthesizing optimal and sub-optimal scheduling policies for this problem have been implemented.

The most recent progress with heuristic depth-first search allowed us to synthesize adaptive schedulers for problems with 400 tasks and up to 20 conditions.

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