Multi-machine energy-aware scheduling

David Van Den Dooren (), Thomas Sys (), Túlio A. M. Toffolo (), Tony Wauters () and Greet Vanden Berghe ()
Additional contact information
David Van Den Dooren: KU Leuven
Thomas Sys: KU Leuven
Túlio A. M. Toffolo: KU Leuven
Tony Wauters: KU Leuven
Greet Vanden Berghe: KU Leuven

EURO Journal on Computational Optimization, 2017, vol. 5, issue 1, No 11, 285-307

Abstract: Abstract 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.

Keywords: Scheduling; Multi-machine; Energy optimisation; Heuristics; 90–08 Computational methods; 90B35 Scheduling theory; deterministic; 90C11 Mixed integer programming; 90C59 Approximation methods and heuristics; 68T20 Problem solving (heuristics; search strategies; etc.) (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

Downloads: (external link)
http://link.springer.com/10.1007/s13675-016-0072-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:eurjco:v:5:y:2017:i:1:d:10.1007_s13675-016-0072-0

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/13675

DOI: 10.1007/s13675-016-0072-0

Access Statistics for this article

EURO Journal on Computational Optimization is currently edited by Martine C. Labbé

More articles in EURO Journal on Computational Optimization from Springer, EURO - The Association of European Operational Research Societies
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().