Energy Saving in Flow-Shop Scheduling Management: An Improved Multiobjective Model Based on Grey Wolf Optimization Algorithm Article Swipe
Lvjiang Yin
,
Mei-Er Zhuang
,
Jing Jia
,
Huan Wang
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1155/2020/9462048
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1155/2020/9462048
Currently, energy saving is increasingly important. During the production procedure, energy saving can be achieved if the operational method and machine infrastructure are improved, but it also increases the complexity of flow-shop scheduling. Actually, as one of the data mining technologies, Grey Wolf Optimization Algorithm is widely applied to various mathematical problems in engineering. However, due to the immaturity of this algorithm, it still has some defects. Therefore, we propose an improved multiobjective model based on Grey Wolf Optimization Algorithm related to Kalman filter and reinforcement learning operator, where Kalman filter is introduced to make the solution set closer to the Pareto optimal front end. By means of reinforcement learning operator, the convergence speed and solving ability of the algorithm can be improved. After testing six benchmark functions, the results show that it is better than that of the original algorithm and other comparison algorithms in terms of search accuracy and solution set diversity. The improved multiobjective model based on Grey Wolf Optimization Algorithm proposed in this paper is conducive to solving energy saving problems in flow-shop scheduling problem, and it is of great practical value in engineering and management.
Related Topics
Concepts
Mathematical optimization
Computer science
Scheduling (production processes)
Multi-objective optimization
Pareto principle
Kalman filter
Convergence (economics)
Benchmark (surveying)
Flow shop scheduling
Job shop scheduling
Algorithm
Mathematics
Artificial intelligence
Schedule
Operating system
Geodesy
Economics
Geography
Economic growth
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2020/9462048
- https://downloads.hindawi.com/journals/mpe/2020/9462048.pdf
- OA Status
- hybrid
- Cited By
- 44
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3095748321
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3095748321Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2020/9462048Digital Object Identifier
- Title
-
Energy Saving in Flow-Shop Scheduling Management: An Improved Multiobjective Model Based on Grey Wolf Optimization AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-13Full publication date if available
- Authors
-
Lvjiang Yin, Mei-Er Zhuang, Jing Jia, Huan WangList of authors in order
- Landing page
-
https://doi.org/10.1155/2020/9462048Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/mpe/2020/9462048.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/mpe/2020/9462048.pdfDirect OA link when available
- Concepts
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Mathematical optimization, Computer science, Scheduling (production processes), Multi-objective optimization, Pareto principle, Kalman filter, Convergence (economics), Benchmark (surveying), Flow shop scheduling, Job shop scheduling, Algorithm, Mathematics, Artificial intelligence, Schedule, Operating system, Geodesy, Economics, Geography, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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44Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 32, 2023: 1, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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