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
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