An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling
Joint Authors
Wu, Peiliang
Chen, Wenbai
Mao, Bingyi
Yu, Hongnian
Yang, Qingyu
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-28
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Due to the NP-hard nature, the permutation flowshop scheduling problem (PFSSP) is a fundamental issue for Industry 4.0, especially under higher productivity, efficiency, and self-managing systems.
This paper proposes an improved genetic-shuffled frog-leaping algorithm (IGSFLA) to solve the permutation flowshop scheduling problem.
In the proposed IGSFLA, the optimal initial frog (individual) in the initialized group is generated according to the heuristic optimal-insert method with fitness constrain.
The crossover mechanism is applied to both the subgroup and the global group to avoid the local optimal solutions and accelerate the evolution.
To evolve the frogs with the same optimal fitness more outstanding, the disturbance mechanism is applied to obtain the optimal frog of the whole group at the initialization step and the optimal frog of the subgroup at the searching step.
The mathematical model of PFSSP is established with the minimum production cycle (makespan) as the objective function, the fitness of frog is given, and the IGSFLA-based PFSSP is proposed.
Experimental results have been given and analyzed, showing that IGSFLA not only provides the optimal scheduling performance but also converges effectively.
American Psychological Association (APA)
Wu, Peiliang& Yang, Qingyu& Chen, Wenbai& Mao, Bingyi& Yu, Hongnian. 2020. An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling. Complexity،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1141240
Modern Language Association (MLA)
Wu, Peiliang…[et al.]. An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling. Complexity No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1141240
American Medical Association (AMA)
Wu, Peiliang& Yang, Qingyu& Chen, Wenbai& Mao, Bingyi& Yu, Hongnian. An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling. Complexity. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1141240
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141240