An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling
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 permutati...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/3450180 |
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| author | Peiliang Wu Qingyu Yang Wenbai Chen Bingyi Mao Hongnian Yu |
| author_facet | Peiliang Wu Qingyu Yang Wenbai Chen Bingyi Mao Hongnian Yu |
| author_sort | Peiliang Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f95956cf7e3d40798f4531c8451ef601 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-f95956cf7e3d40798f4531c8451ef6012025-02-03T06:46:20ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/34501803450180An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop SchedulingPeiliang Wu0Qingyu Yang1Wenbai Chen2Bingyi Mao3Hongnian Yu4School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Automation, Beijing Information Science & Technology University, Beijing 100101, ChinaSchool of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UKDue 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.http://dx.doi.org/10.1155/2020/3450180 |
| spellingShingle | Peiliang Wu Qingyu Yang Wenbai Chen Bingyi Mao Hongnian Yu An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling Complexity |
| title | An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling |
| title_full | An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling |
| title_fullStr | An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling |
| title_full_unstemmed | An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling |
| title_short | An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling |
| title_sort | improved genetic shuffled frog leaping algorithm for permutation flowshop scheduling |
| url | http://dx.doi.org/10.1155/2020/3450180 |
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