Ralf Borndörfer, Guillaume Sagnol
Daniel Schmidt genannt Waldschmidt (TU)
01.01.2019 – 31.12.2022
Scheduling jobs of stochastic duration is ideally done by means of fully adaptive policies. In applications such as surgery scheduling, however, highly volatile regimes cannot be implemented. We will study appropriate scheduling policies for such situations, both from a theoretical and a computational perspective.
We are conducting a fine-grained analysis of the adaptivity trade-off for machine scheduling problems, by considering classes of semi-adaptive policies that can be described with a single parameter, and that interpolates fully adaptive non-anticipative policies and fixed assignment policies. The goal is to get the most out of the performance of adaptive policies,while keeping the stability properties of fixed assignments. Such policies could easily be implemented in stochastic envoronments like hospitals.
From a more practical point of view, we are also investigating the use of reinforcement learning to compute such policies.
Reassigning jobs to free machines only at integer multiples of (αT+δ) starting with a delay δ
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