Energy aware scheduling in flexible flow shops with hybrid particle swarm optimization

This paper integrates energy awareness in the flexible flow shop scheduling system, where two objectives are minimized simultaneously: total tardiness and electric power costs. We also consider practical settings including variable processing speeds and time-of-use (TOU) electricity prices. A novel hybrid particle swarm optimization (HPSO) algorithm is developed which incorporates several distinguishing features: Particles are represented based on job operation and machine assignment, which are updated directly in the discrete domain. More importantly, we introduce a multi-objective tabu search procedure and a position based crossover operator to balance global exploration and local exploitation. Experiments are conducted to verify the performance of the proposed HPSO algorithm compared to the well-known approaches in the literature. Results show the significance of HPSO in terms of the number and quality of non-dominated solutions and computational efficiency. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This paper investigates the integration of energy awareness in the flexible flow shop scheduling system, utilizing a hybrid particle swarm optimization algorithm to simultaneously minimize total tardiness and electric power costs. Experimental results demonstrate the significance of the HPSO algorithm in terms of the number and quality of non-dominated solutions and computational efficiency.