IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v11y2020i5d10.1007_s13198-020-01020-8.html
   My bibliography  Save this article

A bi-objective model for redundancy allocation problem in designing server farms: mathematical formulation and solution approaches

Author

Listed:
  • Vahid Baradaran

    (Islamic Azad University, Tehran North Branch)

  • Amir Hossein Hosseinian

    (Islamic Azad University, Tehran North Branch)

Abstract

Communication has a remarkable and strategic role for the success of every enterprise. Nowadays, computer networks can be used as efficient tools to create connections between departments of an organization and to communicate with customers. Therefore, it is crucial for companies with IT-based activities to use computer networks so as to increase their efficiencies. In this paper, a bi-objective mathematical formulation is proposed for server farms. A server farm is a cluster of computer systems connected together to provide services to an organization and its customers. This model optimizes reliability and cost of server farms, concurrently. This problem is called the redundancy allocation problem that belongs to the class of NP-hard problems. Therefore, three meta-heuristics namely non-dominated sorting genetic algorithm II, pareto envelope-based selection algorithm II, and strength pareto evolutionary algorithm II have been hired to obtain feasible solutions in a reasonable computation time. To evaluate the performances of meta-heuristic methods, the ε-constraint method as an exact algorithm has also been used. The outputs demonstrate appropriate effectiveness of the employed algorithms.

Suggested Citation

  • Vahid Baradaran & Amir Hossein Hosseinian, 2020. "A bi-objective model for redundancy allocation problem in designing server farms: mathematical formulation and solution approaches," 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(5), pages 935-952, October.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:5:d:10.1007_s13198-020-01020-8
    DOI: 10.1007/s13198-020-01020-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-020-01020-8
    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/s13198-020-01020-8?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. Wang, Chaonan & Xing, Liudong & Amari, Suprasad V. & Tang, Bo, 2020. "Efficient reliability analysis of dynamic k-out-of-n heterogeneous phased-mission systems," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. Ouzineb, Mohamed & Nourelfath, Mustapha & Gendreau, Michel, 2008. "Tabu search for the redundancy allocation problem of homogenous series–parallel multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1257-1272.
    3. Deb, Kalyanmoy & Tiwari, Santosh, 2008. "Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1062-1087, March.
    4. Dolatshahi-Zand, Ali & Khalili-Damghani, Kaveh, 2015. "Design of SCADA water resource management control center by a bi-objective redundancy allocation problem and particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 11-21.
    5. M. Yazdani & M. Zandieh & R. Tavakkoli-Moghaddam, 2019. "Evolutionary algorithms for multi-objective dual-resource constrained flexible job-shop scheduling problem," OPSEARCH, Springer;Operational Research Society of India, vol. 56(3), pages 983-1006, September.
    6. Nahas, Nabil & Nourelfath, Mustapha & Ait-Kadi, Daoud, 2007. "Coupling ant colony and the degraded ceiling algorithm for the redundancy allocation problem of series–parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 92(2), pages 211-222.
    7. Khalili-Damghani, Kaveh & Abtahi, Amir-Reza & Tavana, Madjid, 2013. "A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 58-75.
    8. Chang, Kuo-Hao & Kuo, Po-Yi, 2018. "An efficient simulation optimization method for the generalized redundancy allocation problem," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1094-1101.
    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. Gholinezhad, Hadi & Zeinal Hamadani, Ali, 2017. "A new model for the redundancy allocation problem with component mixing and mixed redundancy strategy," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 66-73.
    2. Kayedpour, Farjam & Amiri, Maghsoud & Rafizadeh, Mahmoud & Shahryari Nia, Arash, 2017. "Multi-objective redundancy allocation problem for a system with repairable components considering instantaneous availability and strategy selection," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 11-20.
    3. Zhang, Enze & Chen, Qingwei, 2016. "Multi-objective reliability redundancy allocation in an interval environment using particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 83-92.
    4. Kong, Xiangyong & Gao, Liqun & Ouyang, Haibin & Li, Steven, 2015. "Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 147-158.
    5. Feizabadi, Mohammad & Jahromi, Abdolhamid Eshraghniaye, 2017. "A new model for reliability optimization of series-parallel systems with non-homogeneous components," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 101-112.
    6. Peiravi, Abdossaber & Nourelfath, Mustapha & Zanjani, Masoumeh Kazemi, 2022. "Universal redundancy strategy for system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Nourelfath, Mustapha & Châtelet, Eric & Nahas, Nabil, 2012. "Joint redundancy and imperfect preventive maintenance optimization for series–parallel multi-state degraded systems," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 51-60.
    8. Seyed Mohsen Mousavi & Najmeh Alikar & Madjid Tavana & Debora Di Caprio, 2019. "An improved particle swarm optimization model for solving homogeneous discounted series-parallel redundancy allocation problems," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1175-1194, March.
    9. Attar, Ahmad & Raissi, Sadigh & Khalili-Damghani, Kaveh, 2017. "A simulation-based optimization approach for free distributed repairable multi-state availability-redundancy allocation problems," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 177-191.
    10. Chambari, Amirhossain & Najafi, Amir Abbas & Rahmati, Seyed Habib A. & Karimi, Aida, 2013. "An efficient simulated annealing algorithm for the redundancy allocation problem with a choice of redundancy strategies," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 158-164.
    11. Zhang, Enze & Wu, Yifei & Chen, Qingwei, 2014. "A practical approach for solving multi-objective reliability redundancy allocation problems using extended bare-bones particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 127(C), pages 65-76.
    12. Huang, Xianzhen & Coolen, Frank P.A. & Coolen-Maturi, Tahani, 2019. "A heuristic survival signature based approach for reliability-redundancy allocation," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 511-517.
    13. Xu, Yue & Pi, Dechang & Yang, Shengxiang & Chen, Yang, 2021. "A novel discrete bat algorithm for heterogeneous redundancy allocation of multi-state systems subject to probabilistic common-cause failure," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    14. Zaretalab, Arash & Hajipour, Vahid & Tavana, Madjid, 2020. "Redundancy allocation problem with multi-state component systems and reliable supplier selection," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    15. 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).
    16. Coelho, Leandro dos Santos, 2009. "An efficient particle swarm approach for mixed-integer programming in reliability–redundancy optimization applications," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 830-837.
    17. Dobani, Ehsan Ramezani & Ardakan, Mostafa Abouei & Davari-Ardakani, Hamed & Juybari, Mohammad N., 2019. "RRAP-CM: A new reliability-redundancy allocation problem with heterogeneous components," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    18. Du, Mengyu & Li, Yan-Fu, 2020. "An investigation of new local search strategies in memetic algorithm for redundancy allocation in multi-state series-parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    19. Liagkouras, Konstantinos & Metaxiotis, Konstantinos, 2021. "Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1019-1036.
    20. Chang, Kuo-Hao & Kuo, Po-Yi, 2018. "An efficient simulation optimization method for the generalized redundancy allocation problem," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1094-1101.

    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:11:y:2020:i:5:d:10.1007_s13198-020-01020-8. 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.