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Automated parameter selection of scheduling algorithms using machine learning techniques

Published: 28 September 2017 Publication History

Abstract

The work describes the effort to automatically select scheduling algorithms and generate corresponding parameters for new problem instances based on the results obtained for similar problem instances that have been extensively investigated. The effort tries to vastly reduce the development cycle of optimization algorithms as parameter tuning is usually more time consuming that implementing the algorithm or model. We investigated various heuristic methods for hyper-parameter selection and evaluated different machine learning methods. The results are very promising as selecting the top 5% combination of algorithms and parameters manages to consistently achieve results that are in the top 10% of the generated solutions, if full parameter and algorithm execution is performed.

References

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Alefragis, P. et al. 2000. Parallel Integer Optimization for Crew Scheduling. Annals of Operations Research. 99, 1 (2000), 141--166.
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Bergstra, J. and Bengio, Y. 2012. Random Search for Hyper-Parameter Optimization. Journal of Machine Learning Research. 13, (2012), 281--305.
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Carter, M.W. et al. 1996. Examination timetabling: Algorithmic strategies and applications. Journal of the Operational Research Society. (1996), 373--383.
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Emeretlis, A. et al. 2014. A hybrid ILP-CP model for mapping Directed Acyclic Task Graphs to multicore architectures. IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW) (2014), 176--182.
[5]
Kristiansen, S. and Stidsen, T.R. 2013. A Comprehensive Study of Educational Timetabling, a Survey. A Comprehensive Study of Educational Timetabling - a Survey. 978-87-931, November (2013), 72.
[6]
Lessmann, S. et al. 2011. Tuning metaheuristics: A data mining based approach for particle swarm optimization. Expert Systems with Applications. 38, 10 (2011), 12826--12838.
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Stripf, T. et al. 2013. Compiling Scilab to high performance embedded multicore systems. Microprocessors and Microsystems. 37, 8 PARTC (2013), 1033--1049.
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Valouxis, C. et al. 2012. A systematic two phase approach for the nurse rostering problem. European Journal of Operational Research. 219, 2 (2012), 425-- 433.

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  • (2025)From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling ProblemsComputation10.3390/computation1301001013:1(10)Online publication date: 3-Jan-2025

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PCI '17: Proceedings of the 21st Pan-Hellenic Conference on Informatics
September 2017
322 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

In-Cooperation

  • Greek Com Soc: Greek Computer Society
  • University of Thessaly: University of Thessaly, Volos, Greece

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 September 2017

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Author Tags

  1. Machine Learning
  2. Scheduling
  3. Timetabling

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  • Research-article
  • Research
  • Refereed limited

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PCI 2017
PCI 2017: 21st PAN-HELLENIC CONFERENCE ON INFORMATICS
September 28 - 30, 2017
Larissa, Greece

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Overall Acceptance Rate 190 of 390 submissions, 49%

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Cited By

View all
  • (2025)From Integer Programming to Machine Learning: A Technical Review on Solving University Timetabling ProblemsComputation10.3390/computation1301001013:1(10)Online publication date: 3-Jan-2025

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