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 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.