A Survey on Approximation Algorithms for Scheduling with Machine Unavailability

  • Florian Diedrich
  • Klaus Jansen
  • Ulrich M. Schwarz
  • Denis Trystram
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5515)


In this chapter we present recent contributions in the field of sequential job scheduling on network machines which work in parallel; these are subject to temporary unavailability. This unavailability can be either unforeseeable (online models) or known a priori (offline models). For the online models we are mainly interested in preemptive schedules for problem formulations where the machine unavailability is given by a probabilistic model; objectives of interest here are the sum of completion times and the makespan. Here, the non-preemptive case is essentially intractable. For the offline models we are interested in non-preemptive schedules where we consider the makespan objective; we present approximation algorithms which are complemented by suitable inapproximability results. Here, the preemptive model is polynomial-time solvable for large classes of settings.


Approximation Algorithm Completion Time Knapsack Problem Competitive Ratio Online Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Florian Diedrich
    • 1
  • Klaus Jansen
    • 1
  • Ulrich M. Schwarz
    • 1
  • Denis Trystram
    • 2
  1. 1.Institut für InformatikChristian-Albrechts-Universität zu KielKielGermany
  2. 2.LIG – Grenoble UniversityMontbonnot Saint-MartinFrance

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