Abstract
In the paper, we investigate theoretical and practical aspects of distributed computing for simulated annealing algorithms applied to the problem of scheduling l jobs on m machines. Given n = l · m, the total number of tasks, O(n 3) processors and an upper bound Λ = Λ(l, m) of the objective function, the expected run-times of parallelized versions of our heuristics [14] are O(n · log n · log Λ) for the exponential cooling schedule and O(n2 · log3/2 n · m1/2 · log Λ) for the hyperbolic one. For Markov chains of constant length, the results imply a polylogarithmic run-time O(log n · log(l + m)) for the exponential schedule, where we employ Λ ≤ O(l + m), see Leighton et al. [10]. We implemented a distributed version of our sequential heuristics and first computational experiments on benchmark instances are presented.
Research partially supported by the Strategic Research Programme at CUHK under Grant No. SRP 9505, by a Hong Kong Government RGC Earmarked Grant, Ref.No. CUHK 4367/99E, and by the Germany/Hong Kong Joint Research Scheme under Grant Nos. D/9800710, GHK 99/02 and GHK 00/08.
On leave from IBM T.J. Watson Research Center, P.O. Box 218, Yorktown Heights, N.Y., U.S.A.
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Albrecht, A., Der, U., Steinhöfel, K., Wong, CK. (2000). Distributed Simulated Annealing for Job Shop Scheduling. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_24
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DOI: https://doi.org/10.1007/3-540-45356-3_24
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