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

Research output: Contribution to journalResearch articleContributedpeer-review



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.


Original languageEnglish
Article number105088
Number of pages17
JournalComputers & operations research
Publication statusPublished - Jan 2021

External IDs

Scopus 85090422768
ORCID /0000-0003-4711-2184/work/115248265



  • Flexible flow shop, Energy aware scheduling, Multi-objective optimization, TOU tariffs, MULTIOBJECTIVE GENETIC ALGORITHM, TOTAL WEIGHTED TARDINESS, CONSUMPTION, TIME, MAKESPAN, SEARCH