Shioura, A, Shakhlevich, NV and Strusevich, VA (2017) Machine Speed Scaling by Adapting Methods for Convex Optimization with Submodular Constraints. INFORMS Journal on Computing, 29 (4). pp. 724-736. ISSN 1091-9856
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(n2) time.
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Copyright, Publisher and Additional Information: | © 2017, The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, distribute, transmit and adapt this work, but you must attribute this work as “INFORMS Journal on Computing. Copyright © 2017 The Author(s). https://doi.org/10.1287/ijoc.2017.0758, used under a Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/.” | ||||
Keywords: | analysis of algorithms; computational complexity; programming; nonlinear; production-scheduling: single machine; production-scheduling: multiple machines | ||||
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Institution: | The University of Leeds | ||||
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) | ||||
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Depositing User: | Symplectic Publications | ||||
Date Deposited: | 14 Mar 2017 13:14 | ||||
Last Modified: | 23 Jun 2023 22:25 | ||||
Status: | Published | ||||
Publisher: | INFORMS | ||||
Identification Number: | https://doi.org/10.1287/ijoc.2017.0758 |