Machine Speed Scaling by Adapting Methods for Convex Optimization with Submodular Constraints

Akiyoshi Shioura (), Natalia V. Shakhlevich () and Vitaly A. Strusevich ()
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Akiyoshi Shioura: Department of Industrial Engineering and Economics, Tokyo Institute of Technology, Tokyo 152, Japan
Natalia V. Shakhlevich: School of Computing, University of Leeds, Leeds LS2 9JT, United Kingdom
Vitaly A. Strusevich: Department of Mathematical Sciences, University of Greenwich, Old Royal Naval College, London SE10 9LS, United Kingdom

INFORMS Journal on Computing, 2017, vol. 29, issue 4, 724-736

Abstract: In this paper, we propose a new methodology for the speed-scaling problem based on its link to scheduling with controllable processing times and submodular optimization. It results in faster algorithms for traditional speed-scaling models, characterized by a common speed/energy function. Additionally, it efficiently handles the most general models with job-dependent speed/energy functions with single and multiple machines. To the best of our knowledge, this has not been addressed prior to this study. In particular, the general version of the single-machine case is solvable by the new technique in O ( n 2 ) time.

Keywords: analysis of algorithms; computational complexity; programming; nonlinear; production-scheduling: single machine; production-scheduling: multiple machine (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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