IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i5d10.1007_s13198-022-01659-5.html
   My bibliography  Save this article

Reliability of the illumination of the darkroom with different scenario of the switching methods in uncertain environment

Author

Listed:
  • Nabaranjan Bhattacharyee

    (Sidho-Kanho-Birsha University)

  • Nirmal Kumar

    (Purulia Polytechnic)

  • Sanat Kumar Mahato

    (Sidho-Kanho-Birsha University)

  • Puja Supakar

    (Sidho-Kanho-Birsha University)

Abstract

Several applications of darkroom is observed in modern technological applications. A darkroom can be made completely dark to allow the processing of the light-sensitive photographic materials, including film and photographic paper. Various equipments are used in the darkroom, including an enlarger, baths containing chemicals and running water. It is used to process photographic film, to make prints and to carry out other associated tasks. Also, in non-destructive testing such as magnetic particle inspection a darkroom is essential. Here, we studied the reliability of the illumination of a darkroom under different scenarios of the lighting systems. We have developed three models for illuminating the darkroom with a single operating switch with two tube lights, a single operating switch with multiple tube lights and multiple switches operating individual tube light. We have designed the simple unconstrained model and also the constrained redundancy allocation models. Moreover, we have considered the uncertainty models in the form of intuitionistic fuzzy number as it is the more generalization of the fuzzy numbers. In these imprecise models, the parameters along with the component reliabilities are taken as triangular intuitionistic fuzzy numbers. Before solving these imprecise models, we have crispified these into deterministic form by using suitable and efficient crispification method (BADD). The constrained redundancy allocation problems of integer programming type are obtained in each model and we have implemented evolutionary algorithm WQPSO to solve these problems successfully. Result analysis with comparison has been presented with the recommendation that the third model is more reliable in illuminating the darkroom.

Suggested Citation

  • Nabaranjan Bhattacharyee & Nirmal Kumar & Sanat Kumar Mahato & Puja Supakar, 2022. "Reliability of the illumination of the darkroom with different scenario of the switching methods in uncertain environment," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2482-2499, October.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:5:d:10.1007_s13198-022-01659-5
    DOI: 10.1007/s13198-022-01659-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-022-01659-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-022-01659-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Garima Goswami & Pankaj Kumar Goswami, 2021. "A design analysis and implementation of PI, PID and fuzzy supervised shunt APF at nonlinear load application to improve power quality and system reliability," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(6), pages 1247-1261, December.
    2. Gurwinder Singh & Amarinder Singh, 2021. "Solving fixed-charge transportation problem using a modified particle swarm optimization algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(6), pages 1073-1086, December.
    3. Mohammad Nadjafi & Mohammad Ali Farsi, 2021. "Reliability analysis of system with timing functional dependency using fuzzy-bathtub failure rates," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(5), pages 919-930, October.
    4. Mellal, Mohamed Arezki & Zio, Enrico, 2020. "System reliability-redundancy optimization with cold-standby strategy by an enhanced nest cuckoo optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    5. Yu, Haiyue & Wu, Xinyang & Wu, Xiaoyue, 2020. "An extended object-oriented petri net model for mission reliability evaluation of phased-mission system with time redundancy," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    6. Jagdish Chand Bansal & Harish Sharma & Kusum Deep & Kedar Nath Das & Atulya Nagar, 2018. "Special issue on swarm intelligence and its applications to engineering," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(4), pages 739-740, August.
    7. Wang, Wei & Lin, Mingqiang & Fu, Yongnian & Luo, Xiaoping & Chen, Hanghang, 2020. "Multi-objective optimization of reliability-redundancy allocation problem for multi-type production systems considering redundancy strategies," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    8. Sarita Devi & Deepika Garg, 2020. "Hybrid genetic and particle swarm algorithm: redundancy allocation problem," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 313-319, April.
    9. Laxminarayan Sahoo & Asoke Kumar Bhunia, 2021. "Optimization of plant location problem in interval domain via particle swarm optimization," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(6), pages 1094-1105, December.
    10. Peiravi, Abdossaber & Ardakan, Mostafa Abouei & Zio, Enrico, 2020. "A new Markov-based model for reliability optimization problems with mixed redundancy strategy," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    11. Cao, Ran & Coit, David W. & Hou, Wei & Yang, Yushu, 2020. "Game theory based solution selection for multi-objective redundancy allocation in interval-valued problem parameters," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Shuai & Chi, Xuefen & Yu, Baozhu, 2022. "An improved particle swarm optimization algorithm for the reliability–redundancy allocation problem with global reliability," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Zhang, Hanxiao & Sun, Muxia & Li, Yan-Fu, 2022. "Reliability–redundancy allocation problem in multi-state flow network: Minimal cut-based approximation scheme," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. Peiravi, Abdossaber & Nourelfath, Mustapha & Zanjani, Masoumeh Kazemi, 2022. "Redundancy strategies assessment and optimization of k-out-of-n systems based on Markov chains and genetic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. Wang, Chaonan & Wang, Xiaolei & Xing, Liudong & Guan, Quanlong & Yang, Chunhui & Yu, Min, 2021. "A Fast and Accurate Reliability Approximation Method for Heterogeneous Cold Standby Sparing Systems," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    5. Peiravi, Abdossaber & Nourelfath, Mustapha & Zanjani, Masoumeh Kazemi, 2022. "Universal redundancy strategy for system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    6. Nath, Rahul & Muhuri, Pranab K., 2022. "Evolutionary Optimization based Solution approaches for Many Objective Reliability-Redundancy Allocation Problem," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    7. Anushri Maji & Asoke Kumar Bhunia & Shyamal Kumar Mondal, 2022. "A production-reliability-inventory model for a series-parallel system with mixed strategy considering shortage, warranty period, credit period in crisp and stochastic sense," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 862-907, September.
    8. Hsieh, Tsung-Jung, 2023. "Performance indicator-based multi-objective reliability optimization for multi-type production systems with heterogeneous machines," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Enrico Zio & Hadi Gholinezhad, 2023. "Redundancy Allocation of Components with Time-Dependent Failure Rates," Mathematics, MDPI, vol. 11(16), pages 1-27, August.
    10. Sedaghat, Niloofar & Ardakan, Mostafa Abouei, 2021. "G-mixed: A new strategy for redundant components in reliability optimization problems," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    11. Myung-Ki Baek & Heungseob Kim, 2024. "Lifetime Distribution for a Mixed Redundant System with Imperfect Switch and Components Having Phase–Type Time-to-Failure Distribution," Mathematics, MDPI, vol. 12(8), pages 1-17, April.
    12. Xiang, Jianwen & Wang, Zixiang & Wu, Chuanli & Zhao, Dongdong & Tian, Jing, 2022. "Optimal redundancies of parallel–series systems in irrelevancy coverage model," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    13. Zaretalab, Arash & Sharifi, Mani & Guilani, Pedram Pourkarim & Taghipour, Sharareh & Niaki, Seyed Taghi Akhavan, 2022. "A multi-objective model for optimizing the redundancy allocation, component supplier selection, and reliable activities for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    14. Ling, Chunyan & Yang, Lechang & Feng, Kaixuan & Kuo, Way, 2023. "Survival signature based robust redundancy allocation under imprecise probability," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    15. Wang, Rongxi & Li, Yufan & Xu, Jinjin & Wang, Zhen & Gao, Jianmin, 2022. "F2G: A hybrid fault-function graphical model for reliability analysis of complex equipment with coupled faults," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    16. Jie, Ke-Wei & Liu, San-Yang & Sun, Xiao-Jun & Xu, Yun-Cheng, 2023. "A dynamic ripple-spreading algorithm for solving mean–variance of shortest path model in uncertain random networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    17. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2022. "Optimal sequencing of elements activation in 1-out-of-n warm standby system with storage," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    18. Mohamed Kayid & Mashael A. Alshehri, 2023. "Stochastic Comparisons of Lifetimes of Used Standby Systems," Mathematics, MDPI, vol. 11(14), pages 1-17, July.
    19. Ke Chen & Xian Zhao & Qingan Qiu, 2022. "Optimal Task Abort and Maintenance Policies Considering Time Redundancy," Mathematics, MDPI, vol. 10(9), pages 1-16, April.
    20. Tian, Yuan & Han, Minghao & Kulkarni, Chetan & Fink, Olga, 2022. "A prescriptive Dirichlet power allocation policy with deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 224(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:13:y:2022:i:5:d:10.1007_s13198-022-01659-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.