Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm
A hybrid artificial bee colony (HABC) solved the multiobjective low-carbon flexible job-shop scheduling problem (MLFJSP) was proposed in the paper. HABC algorithm uses a two-layer coding method to establish the initial population as the nectar source for the employed bees. In the optimization proces...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9557290/ |
| _version_ | 1818214347076272128 |
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| author | Xiaolin Gu |
| author_facet | Xiaolin Gu |
| author_sort | Xiaolin Gu |
| collection | DOAJ |
| description | A hybrid artificial bee colony (HABC) solved the multiobjective low-carbon flexible job-shop scheduling problem (MLFJSP) was proposed in the paper. HABC algorithm uses a two-layer coding method to establish the initial population as the nectar source for the employed bees. In the optimization process, the employed bee phase and the onlooker bee phase adopt improved crossover mutation strategies and adaptive neighborhood search strategies to generate new nectar sources, and the greedy method is used to retain better solutions. The scout bee update mechanism prevents the algorithm from falling into a local optimum and enhances the convergence of the algorithm. In order to prevent the loss of the optimal solution, the optimization results of each phase are saved in the Pareto archive (PA). Finally, two sets of international standard instances are used to carry out simulation experiments. The simulation results demonstrated that HABC algorithm is an effective algorithm to solve the multiobjective low-carbon flexible job shop scheduling problem. |
| first_indexed | 2024-12-12T06:18:44Z |
| format | Article |
| id | doaj.art-7a88673ce90c4f35b858854f755ac350 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T06:18:44Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7a88673ce90c4f35b858854f755ac3502022-12-22T00:34:57ZengIEEEIEEE Access2169-35362021-01-01913589913591410.1109/ACCESS.2021.31172709557290Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony AlgorithmXiaolin Gu0https://orcid.org/0000-0002-4915-7573School of Computer and Communication Engineering, Dalian Jiaotong University, Dalian, ChinaA hybrid artificial bee colony (HABC) solved the multiobjective low-carbon flexible job-shop scheduling problem (MLFJSP) was proposed in the paper. HABC algorithm uses a two-layer coding method to establish the initial population as the nectar source for the employed bees. In the optimization process, the employed bee phase and the onlooker bee phase adopt improved crossover mutation strategies and adaptive neighborhood search strategies to generate new nectar sources, and the greedy method is used to retain better solutions. The scout bee update mechanism prevents the algorithm from falling into a local optimum and enhances the convergence of the algorithm. In order to prevent the loss of the optimal solution, the optimization results of each phase are saved in the Pareto archive (PA). Finally, two sets of international standard instances are used to carry out simulation experiments. The simulation results demonstrated that HABC algorithm is an effective algorithm to solve the multiobjective low-carbon flexible job shop scheduling problem.https://ieeexplore.ieee.org/document/9557290/Artificial bee colonymultiobjective flexible job-shop problemlow-carbonadaptive variable neighborhood search |
| spellingShingle | Xiaolin Gu Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm IEEE Access Artificial bee colony multiobjective flexible job-shop problem low-carbon adaptive variable neighborhood search |
| title | Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm |
| title_full | Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm |
| title_fullStr | Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm |
| title_full_unstemmed | Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm |
| title_short | Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm |
| title_sort | application research for multiobjective low carbon flexible job shop scheduling problem based on hybrid artificial bee colony algorithm |
| topic | Artificial bee colony multiobjective flexible job-shop problem low-carbon adaptive variable neighborhood search |
| url | https://ieeexplore.ieee.org/document/9557290/ |
| work_keys_str_mv | AT xiaolingu applicationresearchformultiobjectivelowcarbonflexiblejobshopschedulingproblembasedonhybridartificialbeecolonyalgorithm |