Optimization of system reliability for multi-level RAPs in intuitionistic fuzzy atmosphere using genetic algorithm
Modular redundancy plays a significant role for designing a reliable system. This study examines the optimization of system reliability in a modular redundancy allocation problem in crisp and intuitionistic fuzzy atmospheres with the goal of maximizing total system reliability while adhering to reso...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Results in Control and Optimization |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720722000479 |
| _version_ | 1811295094400090112 |
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| author | Rajesh Paramanik Sanat Kumar Mahato Nirmal Kumar Nabaranjan Bhattacharyee Ranjan Kumar Gupta |
| author_facet | Rajesh Paramanik Sanat Kumar Mahato Nirmal Kumar Nabaranjan Bhattacharyee Ranjan Kumar Gupta |
| author_sort | Rajesh Paramanik |
| collection | DOAJ |
| description | Modular redundancy plays a significant role for designing a reliable system. This study examines the optimization of system reliability in a modular redundancy allocation problem in crisp and intuitionistic fuzzy atmospheres with the goal of maximizing total system reliability while adhering to resource restrictions. On applying modular technique on a redundancy allocation problem, one can increase the fault tolerance to the optimum design of the system, making it very effective in terms of component redundancy. So, modular redundancy should be seen as a proper replacement for the old technique of component level redundancy for improving the reliability, efficiency, and maintainability of a working system. The multi-level redundancy allocation problem is being addressed and solved comprehensively in this study using an advanced genetic algorithm (GA) and a penalty function approach in both crisp and intuitionistic fuzzy settings Finally, numerical examples are solved and sensitivity studies are carried out visually to evaluate the consequences of changing key parameters involved in GA. |
| first_indexed | 2024-04-13T05:27:19Z |
| format | Article |
| id | doaj.art-31f8f152419d4c6182c64b395021caf3 |
| institution | Directory Open Access Journal |
| issn | 2666-7207 |
| language | English |
| last_indexed | 2024-04-13T05:27:19Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj.art-31f8f152419d4c6182c64b395021caf32022-12-22T03:00:32ZengElsevierResults in Control and Optimization2666-72072022-12-019100175Optimization of system reliability for multi-level RAPs in intuitionistic fuzzy atmosphere using genetic algorithmRajesh Paramanik0Sanat Kumar Mahato1Nirmal Kumar2Nabaranjan Bhattacharyee3Ranjan Kumar Gupta4Department of Mathematics, Sidho-Kanho-Birsha University, Sainik School, Purulia 723104, India; Corresponding author.Department of Mathematics, Sidho-Kanho-Birsha University, Sainik School, Purulia 723104, IndiaDepartment of Mathematics, The University of Burdwan, Purba Barddhaman 713104, IndiaDepartment of Mathematics, Sidho-Kanho-Birsha University, Sainik School, Purulia 723104, IndiaDepartment of Management, West Bengal State University, North 24 Parganas 700126, IndiaModular redundancy plays a significant role for designing a reliable system. This study examines the optimization of system reliability in a modular redundancy allocation problem in crisp and intuitionistic fuzzy atmospheres with the goal of maximizing total system reliability while adhering to resource restrictions. On applying modular technique on a redundancy allocation problem, one can increase the fault tolerance to the optimum design of the system, making it very effective in terms of component redundancy. So, modular redundancy should be seen as a proper replacement for the old technique of component level redundancy for improving the reliability, efficiency, and maintainability of a working system. The multi-level redundancy allocation problem is being addressed and solved comprehensively in this study using an advanced genetic algorithm (GA) and a penalty function approach in both crisp and intuitionistic fuzzy settings Finally, numerical examples are solved and sensitivity studies are carried out visually to evaluate the consequences of changing key parameters involved in GA.http://www.sciencedirect.com/science/article/pii/S2666720722000479Redundancy allocation problemsIntuitionistic fuzzy numberCenter of area-defuzzification techniqueAdvanced genetic algorithm |
| spellingShingle | Rajesh Paramanik Sanat Kumar Mahato Nirmal Kumar Nabaranjan Bhattacharyee Ranjan Kumar Gupta Optimization of system reliability for multi-level RAPs in intuitionistic fuzzy atmosphere using genetic algorithm Results in Control and Optimization Redundancy allocation problems Intuitionistic fuzzy number Center of area-defuzzification technique Advanced genetic algorithm |
| title | Optimization of system reliability for multi-level RAPs in intuitionistic fuzzy atmosphere using genetic algorithm |
| title_full | Optimization of system reliability for multi-level RAPs in intuitionistic fuzzy atmosphere using genetic algorithm |
| title_fullStr | Optimization of system reliability for multi-level RAPs in intuitionistic fuzzy atmosphere using genetic algorithm |
| title_full_unstemmed | Optimization of system reliability for multi-level RAPs in intuitionistic fuzzy atmosphere using genetic algorithm |
| title_short | Optimization of system reliability for multi-level RAPs in intuitionistic fuzzy atmosphere using genetic algorithm |
| title_sort | optimization of system reliability for multi level raps in intuitionistic fuzzy atmosphere using genetic algorithm |
| topic | Redundancy allocation problems Intuitionistic fuzzy number Center of area-defuzzification technique Advanced genetic algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2666720722000479 |
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