Abstract In this research, the grid scheduling problem has been investigated in order to maximize profit considering the dynamic voltage and frequency scaling technique, customer-centric quality of service and time-dependent energy pricing. Mixed-integer linear programming, constraint programming, a greedy heuristic algorithm along with a hybrid method of genetic algorithm and constraint programming are developed. Some techniques are proposed to improve the efficiency of the presented constraint programming model, and their effectiveness is investigated using a full factorial experiment. Parameters of the proposed hybrid algorithm have been set by Taguchi test. The hybrid meta-heuristic algorithm, with a short execution time, generates solutions of about 18% and 88% better than the best solution of the constraint programming model for large-scale problem instances. The results show that the final profit will be reduced by about 22% if the electricity prices are wrongly considered with a flat rate during the scheduling process.
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