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Getting rid of stochasticity: applicable sometimes

Authors Han Hoogeveen, Marjan Van den Akker



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Han Hoogeveen
Marjan Van den Akker

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Han Hoogeveen and Marjan Van den Akker. Getting rid of stochasticity: applicable sometimes. In Algorithms for Optimization with Incomplete Information. Dagstuhl Seminar Proceedings, Volume 5031, pp. 1-4, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2005)
https://doi.org/10.4230/DagSemProc.05031.12

Abstract

We consider the single-machine scheduling problem of minimizing the number of late jobs. This problem is well-studied and well-understood in case of deterministic processing times. We consider the problem with stochastic processing times, and we show that for a number of probability distributions the problem can be reformulated as a deterministic problem (and solved by the corresponding algorithm) when we use the concept of minimum success probabilities, which is, that we require that the probability that a job complete on time is `big enough'. We further show that we can extend our approach to the case of machines with stochastic output.
Keywords
  • Scheduling
  • sequencing
  • single machine
  • number of late jobs
  • stochastic processing times
  • minimum success probability
  • dynamic programming unreliable machines

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