An energy-efficient multi-objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm
The flow shop scheduling problem has been widely studied in recent years, but the research on multi-objective flow shop scheduling with green indicators is still relatively limited. It is urgent to strengthen the research on effective methods to solve such interesting problems. To consider the econo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2021-06-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878140211023603 |
| _version_ | 1818739481685000192 |
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| author | Wenbin Gu Zhuo Li Min Dai Minghai Yuan |
| author_facet | Wenbin Gu Zhuo Li Min Dai Minghai Yuan |
| author_sort | Wenbin Gu |
| collection | DOAJ |
| description | The flow shop scheduling problem has been widely studied in recent years, but the research on multi-objective flow shop scheduling with green indicators is still relatively limited. It is urgent to strengthen the research on effective methods to solve such interesting problems. To consider the economic and environmental factors simultaneously, the paper investigates the multi-objective permutation flow shop scheduling problems (MOPFSP) which minimizes the makespan and total carbon emissions. Since MOPFSP is proved to be a NP-hard problem for more than two machines. A hybrid cuckoo search algorithm (HCSA) is proposed to solve the problems. Firstly, a largest-order-value method is proposed to enhance the performance of HCS algorithm in the solution space of MOPFSP. Then, an adaptive factor of step size is designed to control the search scopes in the evolution phases. Finally, a multi-neighborhood local search rule is addressed in order to find the optimal sub-regions obtained by the HCSA. Numerical experiments show that HCSA can solve MOPFSP efficiently. |
| first_indexed | 2024-12-18T01:25:31Z |
| format | Article |
| id | doaj.art-8a38a08bb5b649a6a41d88f16d8cabfd |
| institution | Directory Open Access Journal |
| issn | 1687-8140 |
| language | English |
| last_indexed | 2024-12-18T01:25:31Z |
| publishDate | 2021-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-8a38a08bb5b649a6a41d88f16d8cabfd2022-12-21T21:25:45ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402021-06-011310.1177/16878140211023603An energy-efficient multi-objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithmWenbin Gu0Zhuo Li1Min Dai2Minghai Yuan3Department of Mechanical and Electrical Engineering, Hohai University, Changzhou, ChinaDepartment of Mechanical Engineering and Science, Huazhong University of Science and Technology, Wuhan, ChinaCollege of Mechanical Engineering, Yangzhou University, Yangzhou, ChinaDepartment of Mechanical and Electrical Engineering, Hohai University, Changzhou, ChinaThe flow shop scheduling problem has been widely studied in recent years, but the research on multi-objective flow shop scheduling with green indicators is still relatively limited. It is urgent to strengthen the research on effective methods to solve such interesting problems. To consider the economic and environmental factors simultaneously, the paper investigates the multi-objective permutation flow shop scheduling problems (MOPFSP) which minimizes the makespan and total carbon emissions. Since MOPFSP is proved to be a NP-hard problem for more than two machines. A hybrid cuckoo search algorithm (HCSA) is proposed to solve the problems. Firstly, a largest-order-value method is proposed to enhance the performance of HCS algorithm in the solution space of MOPFSP. Then, an adaptive factor of step size is designed to control the search scopes in the evolution phases. Finally, a multi-neighborhood local search rule is addressed in order to find the optimal sub-regions obtained by the HCSA. Numerical experiments show that HCSA can solve MOPFSP efficiently.https://doi.org/10.1177/16878140211023603 |
| spellingShingle | Wenbin Gu Zhuo Li Min Dai Minghai Yuan An energy-efficient multi-objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm Advances in Mechanical Engineering |
| title | An energy-efficient multi-objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm |
| title_full | An energy-efficient multi-objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm |
| title_fullStr | An energy-efficient multi-objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm |
| title_full_unstemmed | An energy-efficient multi-objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm |
| title_short | An energy-efficient multi-objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm |
| title_sort | energy efficient multi objective permutation flow shop scheduling problem using an improved hybrid cuckoo search algorithm |
| url | https://doi.org/10.1177/16878140211023603 |
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