Multi-machine energy-aware scheduling

The traditional set of manufacturing scheduling problems concern general and easy-to-measure economic objectives such as makespan and tardiness. The variable nature of energy costs over the course of the day remains mostly ignored by most previous research. This variability should not be considered...

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Autores principales:	David Van Den Dooren, Thomas Sys, TúlioA.M. Toffolo, Tony Wauters, Greet Vanden Berghe
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2017
Materias:	90–08 Computational methods 90B35 Scheduling theory, deterministic 90C11 Mixed integer programming 90C59 Approximation methods and heuristics 68T20 Problem solving (heuristics, search strategies, etc.) Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/d0d0b60eb9e5463f965670457972cad3
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id	oai:doaj.org-article:d0d0b60eb9e5463f965670457972cad3
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spelling	oai:doaj.org-article:d0d0b60eb9e5463f965670457972cad32021-12-02T05:01:01ZMulti-machine energy-aware scheduling2192-440610.1007/s13675-016-0072-0https://doaj.org/article/d0d0b60eb9e5463f965670457972cad32017-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2192440621000824https://doaj.org/toc/2192-4406The traditional set of manufacturing scheduling problems concern general and easy-to-measure economic objectives such as makespan and tardiness. The variable nature of energy costs over the course of the day remains mostly ignored by most previous research. This variability should not be considered an added complexity, but rather an opportunity for businesses to minimise their energy bill. More effectively scheduling jobs across multiple machines may result in reduced costs despite fixed consumption levels. To this end, this paper proposes a scheduling approach capable of optimising this largely undefined and, consequently, currently unaddressed situation. The proposed multi-machine energy optimisation approach consists of constructive heuristics responsible for generating an initial solution and a late acceptance hill climbing algorithm responsible for improving this initial solution. The combined approach was applied to the scheduling instances of the ICON challenge on Forecasting and Scheduling [The challenge is organized as part of the EU FET-Open: Inductive Constraint Programming (ICON) project (O’Sullivan et al., ICON challenge on forecasting and scheduling. UCC, University College Cork, ICON, Cork. http://iconchallenge.insight-centre.org/challenge-energy, 2014)] whereupon it was proven superior to all other competing algorithms. This achievement highlights the potential of the proposed algorithm insofar as solving the multi-machine energy-aware optimisation problem (MEOP). The new benchmarks are available for further research.David Van Den DoorenThomas SysTúlioA.M. ToffoloTony WautersGreet Vanden BergheElsevierarticle90–08 Computational methods90B35 Scheduling theory, deterministic90C11 Mixed integer programming90C59 Approximation methods and heuristics68T20 Problem solving (heuristics, search strategies, etc.)Applied mathematics. Quantitative methodsT57-57.97Electronic computers. Computer scienceQA75.5-76.95ENEURO Journal on Computational Optimization, Vol 5, Iss 1, Pp 285-307 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	90–08 Computational methods 90B35 Scheduling theory, deterministic 90C11 Mixed integer programming 90C59 Approximation methods and heuristics 68T20 Problem solving (heuristics, search strategies, etc.) Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
spellingShingle	90–08 Computational methods 90B35 Scheduling theory, deterministic 90C11 Mixed integer programming 90C59 Approximation methods and heuristics 68T20 Problem solving (heuristics, search strategies, etc.) Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95 David Van Den Dooren Thomas Sys TúlioA.M. Toffolo Tony Wauters Greet Vanden Berghe Multi-machine energy-aware scheduling
description	The traditional set of manufacturing scheduling problems concern general and easy-to-measure economic objectives such as makespan and tardiness. The variable nature of energy costs over the course of the day remains mostly ignored by most previous research. This variability should not be considered an added complexity, but rather an opportunity for businesses to minimise their energy bill. More effectively scheduling jobs across multiple machines may result in reduced costs despite fixed consumption levels. To this end, this paper proposes a scheduling approach capable of optimising this largely undefined and, consequently, currently unaddressed situation. The proposed multi-machine energy optimisation approach consists of constructive heuristics responsible for generating an initial solution and a late acceptance hill climbing algorithm responsible for improving this initial solution. The combined approach was applied to the scheduling instances of the ICON challenge on Forecasting and Scheduling [The challenge is organized as part of the EU FET-Open: Inductive Constraint Programming (ICON) project (O’Sullivan et al., ICON challenge on forecasting and scheduling. UCC, University College Cork, ICON, Cork. http://iconchallenge.insight-centre.org/challenge-energy, 2014)] whereupon it was proven superior to all other competing algorithms. This achievement highlights the potential of the proposed algorithm insofar as solving the multi-machine energy-aware optimisation problem (MEOP). The new benchmarks are available for further research.
format	article
author	David Van Den Dooren Thomas Sys TúlioA.M. Toffolo Tony Wauters Greet Vanden Berghe
author_facet	David Van Den Dooren Thomas Sys TúlioA.M. Toffolo Tony Wauters Greet Vanden Berghe
author_sort	David Van Den Dooren
title	Multi-machine energy-aware scheduling
title_short	Multi-machine energy-aware scheduling
title_full	Multi-machine energy-aware scheduling
title_fullStr	Multi-machine energy-aware scheduling
title_full_unstemmed	Multi-machine energy-aware scheduling
title_sort	multi-machine energy-aware scheduling
publisher	Elsevier
publishDate	2017
url	https://doaj.org/article/d0d0b60eb9e5463f965670457972cad3
work_keys_str_mv	AT davidvandendooren multimachineenergyawarescheduling AT thomassys multimachineenergyawarescheduling AT tulioamtoffolo multimachineenergyawarescheduling AT tonywauters multimachineenergyawarescheduling AT greetvandenberghe multimachineenergyawarescheduling
_version_	1718400856081039360

Multi-machine energy-aware scheduling

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