Multi-machine energy-aware scheduling
| Autor: | Thomas Sys, Greet Van den Berghe, Tony Wauters, Túlio A. M. Toffolo, David Van Den Dooren |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Rate-monotonic scheduling
Earliest deadline first scheduling 0209 industrial biotechnology Engineering Control and Optimization Operations research Real-time computing 02 engineering and technology Dynamic priority scheduling Management Science and Operations Research 90C11 Mixed integer programming Fair-share scheduling 020901 industrial engineering & automation Fixed-priority pre-emptive scheduling 90C59 Approximation methods and heuristics 68T20 Problem solving (heuristics search strategies etc.) 0505 law T57-57.97 Applied mathematics. Quantitative methods Job shop scheduling business.industry 05 social sciences 90B35 Scheduling theory deterministic QA75.5-76.95 Round-robin scheduling Computational Mathematics Electronic computers. Computer science Modeling and Simulation Two-level scheduling 050501 criminology 90–08 Computational methods business |
| Zdroj: | EURO Journal on Computational Optimization, Vol 5, Iss 1, Pp 285-307 (2017) |
| ISSN: | 2192-4406 |
| DOI: | 10.1007/s13675-016-0072-0 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink