Asymptotical optimality of WSEPT for stochastic online scheduling on uniform machines

Manzhan Gu () and Xiwen Lu ()

Annals of Operations Research, 2011, vol. 191, issue 1, 97-113

Abstract: We study the stochastic online scheduling on m uniform machines with the objective to minimize the expected value of total weighted completion times of a set of jobs that arrive over time. For each job, the processing time is a random variable, and the distribution of processing time is unknown in advance. The actual processing time could be known only when the job is completed. For the problem, we propose a policy which is proved to be asymptotically optimal when the processing times and weights are uniformly bounded, i.e. the relative error of the solution achieved by our policy approaches zero as the number of jobs increases to infinity. Copyright Springer Science+Business Media, LLC 2011

Keywords: Scheduling; Online algorithm; Asymptotical optimality; Uniform machines (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/s10479-011-0985-1

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