Energy aware scheduling in flexible flow shops with hybrid particle swarm optimization. (January 2021)

• Record Type: Journal Article
• Authors: Ding, Junwen; Schulz, Sven; Shen, Liji; Buscher, Udo; Lü, Zhipeng
• Is Part Of: Computers & operations research. Volume 125(2021)
• Publication Date: 2021-01
• Subjects: Flexible flow shop -- Energy aware scheduling -- Multi-objective optimization -- TOU tariffs; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2020.105088
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: Variable speeds and TOU prices are both considered in a bi-criteria FFSP to minimize total tardiness and energy cost. We propose a novel hybrid particle swarm optimization algorithm with integrated tabu search. Substantial energy cost reduction is achieved by speed adjustments and load shifting without deteriorating tardiness. Extensive computational study confirms the efficiency of our approach while comparing with exact and NSGA-II solutions. Abstract: 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.