A parallel genetic algorithm for multi-objective flexible flowshop scheduling in pasta manufacturing
- Author
- Ke Shen (UGent) , Toon De Pessemier (UGent) , Luc Martens (UGent) and Wout Joseph (UGent)
- Organization
- 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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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8728465
- 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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