This paper addresses a bi-objective distribution permutation flow shop scheduling problem (FSP) with setup times aiming to minimize the makespan and the total tardiness. It is very difficult to obtain an optimal solution by using traditional approaches in reasonable computational time. This paper presents an appropriate non-dominated sorting Genetic Algorithm III based on the reference point. The NEH strategy is applied into the generation of the initial solution set. To validate the performance of the NEH strategy improved NSGA III (NNSGA III) on solution quality and diversity level, various test problems are carried out. Three algorithms, including NSGA II, NEH strategy improved NSGA II(NNSGA II) and NNSGA III are utilized to solve this FSP. Experimental results suggest that the proposed NNSGA III outperforms the other algorithms on the Inverse Generation Distance metric, and the distribution of Pareto solutions are improved excellently.
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ASME 2018 13th International Manufacturing Science and Engineering Conference
June 18–22, 2018
College Station, Texas, USA
Conference Sponsors:
- Manufacturing Engineering Division
ISBN:
978-0-7918-5138-8
PROCEEDINGS PAPER
Optimizing Bi-Criteria Permutation Flow Shop Scheduling Problem by Improved NSGA III
Ronghua Meng,
Ronghua Meng
Huazhong University of Science and Technology, Wuhan, China
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Yunqing Rao,
Yunqing Rao
Huazhong University of Science and Technology, Wuhan, China
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Qiang Luo
Qiang Luo
Huazhong University of Science and Technology, Wuhan, China
Search for other works by this author on:
Ronghua Meng
Huazhong University of Science and Technology, Wuhan, China
Yunqing Rao
Huazhong University of Science and Technology, Wuhan, China
Qiang Luo
Huazhong University of Science and Technology, Wuhan, China
Paper No:
MSEC2018-6493, V004T03A030; 5 pages
Published Online:
September 24, 2018
Citation
Meng, R, Rao, Y, & Luo, Q. "Optimizing Bi-Criteria Permutation Flow Shop Scheduling Problem by Improved NSGA III." Proceedings of the ASME 2018 13th International Manufacturing Science and Engineering Conference. Volume 4: Processes. College Station, Texas, USA. June 18–22, 2018. V004T03A030. ASME. https://doi.org/10.1115/MSEC2018-6493
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