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Preemptive models of scheduling with controllable processing times and of scheduling with imprecise computation: A review of solution approaches

Preemptive models of scheduling with controllable processing times and of scheduling with imprecise computation: A review of solution approaches

Shioura, Akiyoshi, Shakhlevich, Natalia V. and Strusevich, Vitaly (2017) Preemptive models of scheduling with controllable processing times and of scheduling with imprecise computation: A review of solution approaches. European Journal of Operational Research, 266 (3). pp. 795-818. ISSN 0377-2217 (doi:https://doi.org/10.1016/j.ejor.2017.08.034)

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Abstract

This paper provides a review of recent results on scheduling with controllable processing times. The stress is on the methodological aspects that include parametric flow techniques and methods for solving mathematical programming problems with submodular constraints. We show that the use of these methodologies yields fast algorithms for solving problems on single machine or parallel machines, with either one or several objective functions. For a wide range of problems with controllable processing times we report algorithms with the running times which match those known for the corresponding problems with fixed processing times. As a by-product, we present the best possible algorithms for a number of problems on parallel machines that are traditionally studied within the body of research on scheduling with imprecise computation.

Item Type: Article
Additional Information: ©2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/ )
Uncontrolled Keywords: Scheduling with controllable processing times, Scheduling with imprecise computation, Flows in networks, Optimization with submodular constraints
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Mathematical Sciences
Last Modified: 16 May 2019 10:18
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: GREAT a
Selected for GREAT 2019: GREAT 4
URI: http://gala.gre.ac.uk/id/eprint/20002

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