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A parallel genetic algorithm for multi-objective flexible flowshop scheduling in pasta manufacturing

Ke Shen (UGent) , Toon De Pessemier (UGent) , Luc Martens (UGent) and Wout Joseph (UGent)
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Abstract
Among the potential road maps to sustainable production, efficient manufacturing scheduling is a promising direction. This paper addresses the lack of knowledge in the scheduling theory by introducing a generalized flexible flow shop model with unrelated parallel machines in each stage. A mixed-integer programming formulation is proposed for such model, solved by a two-phase genetic algorithm (GA), tackling job sequencing and machine allocation in each phase. The algorithm is parallelized with a specialized island model, where the evaluated chromosomes of all generations are preserved to provide the final Pareto-Optimal solutions. The feasibility of our method is demonstrated with a small example from literature, followed with the investigation of the premature convergence issue. Afterwards, the algorithm is applied to a real-sized instance from a Belgium pasta manufacturer. We illustrate how the algorithm converges over iterations to trade-off near-optimal solutions (with 8.50% shorter makespan, 5.24% cheaper energy cost and 6.02% lower labor cost), and how the evaluated candidates distribute in the objective space. A comparison with a NSGA-II implementation is further performed using hypothesis testing, having 5.43%, 0.95% and 2.07% improvement in three sub-objectives mentioned above. Although this paper focuses on scheduling issues, the proposed GA can serve as an efficient method for other multi-objective optimization problems.
Keywords
EVOLUTIONARY ALGORITHMS, SHOP, OPTIMIZATION, CONVERGENCE, 2-STAGE, MODELS, BRANCH, Genetic algorithm, Flexible flowshop, Production scheduling, Multi-objective optimization

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MLA
Shen, Ke, et al. “A Parallel Genetic Algorithm for Multi-Objective Flexible Flowshop Scheduling in Pasta Manufacturing.” COMPUTERS & INDUSTRIAL ENGINEERING, vol. 161, 2021, doi:10.1016/j.cie.2021.107659.
APA
Shen, K., De Pessemier, T., Martens, L., & Joseph, W. (2021). A parallel genetic algorithm for multi-objective flexible flowshop scheduling in pasta manufacturing. COMPUTERS & INDUSTRIAL ENGINEERING, 161. https://doi.org/10.1016/j.cie.2021.107659
Chicago author-date
Shen, Ke, Toon De Pessemier, Luc Martens, and Wout Joseph. 2021. “A Parallel Genetic Algorithm for Multi-Objective Flexible Flowshop Scheduling in Pasta Manufacturing.” COMPUTERS & INDUSTRIAL ENGINEERING 161. https://doi.org/10.1016/j.cie.2021.107659.
Chicago author-date (all authors)
Shen, Ke, Toon De Pessemier, Luc Martens, and Wout Joseph. 2021. “A Parallel Genetic Algorithm for Multi-Objective Flexible Flowshop Scheduling in Pasta Manufacturing.” COMPUTERS & INDUSTRIAL ENGINEERING 161. doi:10.1016/j.cie.2021.107659.
Vancouver
1.
Shen K, De Pessemier T, Martens L, Joseph W. A parallel genetic algorithm for multi-objective flexible flowshop scheduling in pasta manufacturing. COMPUTERS & INDUSTRIAL ENGINEERING. 2021;161.
IEEE
[1]
K. Shen, T. De Pessemier, L. Martens, and W. Joseph, “A parallel genetic algorithm for multi-objective flexible flowshop scheduling in pasta manufacturing,” COMPUTERS & INDUSTRIAL ENGINEERING, vol. 161, 2021.
@article{8728465,
  abstract     = {{Among the potential road maps to sustainable production, efficient manufacturing scheduling is a promising direction. This paper addresses the lack of knowledge in the scheduling theory by introducing a generalized flexible flow shop model with unrelated parallel machines in each stage. A mixed-integer programming formulation is proposed for such model, solved by a two-phase genetic algorithm (GA), tackling job sequencing and machine allocation in each phase. The algorithm is parallelized with a specialized island model, where the evaluated chromosomes of all generations are preserved to provide the final Pareto-Optimal solutions. The feasibility of our method is demonstrated with a small example from literature, followed with the investigation of the premature convergence issue. Afterwards, the algorithm is applied to a real-sized instance from a Belgium pasta manufacturer. We illustrate how the algorithm converges over iterations to trade-off near-optimal solutions (with 8.50% shorter makespan, 5.24% cheaper energy cost and 6.02% lower labor cost), and how the evaluated candidates distribute in the objective space. A comparison with a NSGA-II implementation is further performed using hypothesis testing, having 5.43%, 0.95% and 2.07% improvement in three sub-objectives mentioned above. Although this paper focuses on scheduling issues, the proposed GA can serve as an efficient method for other multi-objective optimization problems.}},
  articleno    = {{107659}},
  author       = {{Shen, Ke and De Pessemier, Toon and Martens, Luc and Joseph, Wout}},
  issn         = {{0360-8352}},
  journal      = {{COMPUTERS & INDUSTRIAL ENGINEERING}},
  keywords     = {{EVOLUTIONARY ALGORITHMS,SHOP,OPTIMIZATION,CONVERGENCE,2-STAGE,MODELS,BRANCH,Genetic algorithm,Flexible flowshop,Production scheduling,Multi-objective optimization}},
  language     = {{eng}},
  pages        = {{11}},
  title        = {{A parallel genetic algorithm for multi-objective flexible flowshop scheduling in pasta manufacturing}},
  url          = {{http://doi.org/10.1016/j.cie.2021.107659}},
  volume       = {{161}},
  year         = {{2021}},
}

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