Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem
In this paper, a novel improved whale optimization algorithm (IWOA), based on the integrated approach, is presented for solving the flexible job shop scheduling problem (FJSP) with the objective of minimizing makespan. First of all, to make the whale optimization algorithm (WOA) adaptive to the FJSP...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/7/5/384 |
| _version_ | 1818952050760744960 |
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| author | Fei Luan Zongyan Cai Shuqiang Wu Tianhua Jiang Fukang Li Jia Yang |
| author_facet | Fei Luan Zongyan Cai Shuqiang Wu Tianhua Jiang Fukang Li Jia Yang |
| author_sort | Fei Luan |
| collection | DOAJ |
| description | In this paper, a novel improved whale optimization algorithm (IWOA), based on the integrated approach, is presented for solving the flexible job shop scheduling problem (FJSP) with the objective of minimizing makespan. First of all, to make the whale optimization algorithm (WOA) adaptive to the FJSP, the conversion method between the whale individual position vector and the scheduling solution is firstly proposed. Secondly, a resultful initialization scheme with certain quality is obtained using chaotic reverse learning (CRL) strategies. Thirdly, a nonlinear convergence factor (NFC) and an adaptive weight (AW) are introduced to balance the abilities of exploitation and exploration of the algorithm. Furthermore, a variable neighborhood search (VNS) operation is performed on the current optimal individual to enhance the accuracy and effectiveness of the local exploration. Experimental results on various benchmark instances show that the proposed IWOA can obtain competitive results compared to the existing algorithms in a short time. |
| first_indexed | 2024-12-20T09:44:13Z |
| format | Article |
| id | doaj.art-8b0787bbfa5d4b5db8eda66868996e71 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-12-20T09:44:13Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-8b0787bbfa5d4b5db8eda66868996e712022-12-21T19:44:47ZengMDPI AGMathematics2227-73902019-04-017538410.3390/math7050384math7050384Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling ProblemFei Luan0Zongyan Cai1Shuqiang Wu2Tianhua Jiang3Fukang Li4Jia Yang5School of Construction Machinery, Chang’an University, Xi’an 710064, ChinaSchool of Construction Machinery, Chang’an University, Xi’an 710064, ChinaSchool of Construction Machinery, Chang’an University, Xi’an 710064, ChinaSchool of Transportation, Ludong University, Yantai 264025, ChinaSchool of Construction Machinery, Chang’an University, Xi’an 710064, ChinaSchool of Construction Machinery, Chang’an University, Xi’an 710064, ChinaIn this paper, a novel improved whale optimization algorithm (IWOA), based on the integrated approach, is presented for solving the flexible job shop scheduling problem (FJSP) with the objective of minimizing makespan. First of all, to make the whale optimization algorithm (WOA) adaptive to the FJSP, the conversion method between the whale individual position vector and the scheduling solution is firstly proposed. Secondly, a resultful initialization scheme with certain quality is obtained using chaotic reverse learning (CRL) strategies. Thirdly, a nonlinear convergence factor (NFC) and an adaptive weight (AW) are introduced to balance the abilities of exploitation and exploration of the algorithm. Furthermore, a variable neighborhood search (VNS) operation is performed on the current optimal individual to enhance the accuracy and effectiveness of the local exploration. Experimental results on various benchmark instances show that the proposed IWOA can obtain competitive results compared to the existing algorithms in a short time.https://www.mdpi.com/2227-7390/7/5/384whale optimization algorithmflexible job shop scheduling problemnonlinear convergence factoradaptive weightvariable neighborhood search |
| spellingShingle | Fei Luan Zongyan Cai Shuqiang Wu Tianhua Jiang Fukang Li Jia Yang Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem Mathematics whale optimization algorithm flexible job shop scheduling problem nonlinear convergence factor adaptive weight variable neighborhood search |
| title | Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem |
| title_full | Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem |
| title_fullStr | Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem |
| title_full_unstemmed | Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem |
| title_short | Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem |
| title_sort | improved whale algorithm for solving the flexible job shop scheduling problem |
| topic | whale optimization algorithm flexible job shop scheduling problem nonlinear convergence factor adaptive weight variable neighborhood search |
| url | https://www.mdpi.com/2227-7390/7/5/384 |
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