An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling

Wu, Peiliang; Yang, Qingyu; Chen, Wenbai; Mao, Bingyi; Yu, Hongnian

Abstract

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.

Citation

Wu, P., Yang, Q., Chen, W., Mao, B., & Yu, H. (2020). An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling. Complexity, 2020, Article 3450180. https://doi.org/10.1155/2020/3450180

• Journal Article Type: Article
• Acceptance Date: Oct 16, 2020
• Online Publication Date: Nov 28, 2020
• Publication Date: Nov 28, 2020
• Deposit Date: Jan 6, 2021
• Publicly Available Date: Jan 6, 2021
• Journal: Complexity
• Print ISSN: 1076-2787
• Electronic ISSN: 1099-0526
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 2020
• Article Number: 3450180
• DOI: https://doi.org/10.1155/2020/3450180
• Public URL: http://researchrepository.napier.ac.uk/Output/2713314