Performance guarantees for scheduling algorithms under perturbed machine speeds

We study two local search algorithms and a greedy algorithm for scheduling. The worstcase performance guarantees are well-known but seem to be contrived and too pessimistic for practical applications. For unrestrictedmachines, Brunsch et al. (2013) showed that the worst-case performance guarantees of these algorithms are not robust if the job sizes are subject to random noise. However, in the case of restricted related machines the worstcase bounds turned out to be robust even in the presence of random noise. We show that if themachine speeds rather than the job sizes are perturbed, one obtains smaller bounds for the performance guarantees also for restricted machines thus yielding a stronger result. © 2014 Elsevier B.V. All rights reserved.