Particle Swarm Optimization for Scheduling to Minimize Tardiness Penalty and Power Cost

Traditional research on machine scheduling focuses on job allocation and sequencing to optimize certain objective functions that are defined in job completion times. With regard to environmental concerns, energy consumption becomes another critical concern in high-performance systems. In this paper, we address the scheduling problem in a multiple machine system where the computing speeds of the machines are allowed to be adjusted during the course of execution. The CPU adjustment capability enables the flexibility for minimizing electricity cost from energy saving by sacrificing job completion times. The decision of the studied problem is to dispatch the jobs to the machine and to determine the job sequence and processing speed of each machine with the objective function comprised of the total weighted job tardiness and the power cost. We give a formal formulation, propose two heuristics, and design a particle swarm optimization (PSO) algorithm. The experiment results provides quality solutions.