IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i8p3410-d349060.html
   My bibliography  Save this article

Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

Author

Listed:
  • Han Lai

    (Chongqing Engineering Laboratory for Detection, Control and Integrated System, Chongqing Technology and Business University, Chongqing 400067, China
    Business School, Sichuan University, Chengdu 610064, China)

  • Huchang Liao

    (Business School, Sichuan University, Chengdu 610064, China
    Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Jonas Šaparauskas

    (Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius LT-10223, Lithuania)

  • Audrius Banaitis

    (Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius LT-10223, Lithuania)

  • Fernando A. F. Ferreira

    (ISCTE Business School, BRU-IUL, University Institute of Lisbon, Avenida das Forças Armadas, 1649-026 Lisbon, Portugal
    Fogelman College of Business and Economics, University of Memphis, Memphis, TN 38152-3120, USA)

  • Abdullah Al-Barakati

    (Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

The sustainable development of cloud service providers (CSPs) is a significant multiple criteria decision making (MCDM) problem, involving the intrinsic relations among multiple alternatives, (quantitative and qualitative) decision criteria and decision-experts for the selection of trustworthy CSPs. Most existing MCDM methods for CSP selection incorporated only one normalization technique in benefit and cost criteria, which would mislead the decision results and limit the applications of these methods. In addition, these methods did not consider the reliability of information given by decision-makers. Given these research gaps, this study introduces a Z-number-based double normalization-based multiple aggregation (DNMA) method to tackle quantitative and qualitative criteria in forms of benefit, cost, and target types for sustainable CSP development. We extend the original DNMA method to the Z-number environment to handle the uncertain and unreliability information of decision-makers. To make trade-offs between normalized criteria values, we develop a Gini-coefficient based weighting method to replace the mean-square-based weighting method used in the original DNMA method to enhance the applicability and isotonicity of the DNMA method. A case study is conducted to demonstrate the effectiveness of the proposed method. Furthermore, comparative analysis and sensitivity analysis are implemented to test the stability and applicability of the proposed method.

Suggested Citation

  • Han Lai & Huchang Liao & Jonas Šaparauskas & Audrius Banaitis & Fernando A. F. Ferreira & Abdullah Al-Barakati, 2020. "Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3410-:d:349060
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/8/3410/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/8/3410/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liao, Huchang & Wu, Xingli, 2020. "DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making," Omega, Elsevier, vol. 94(C).
    2. Mi, Xiaomei & Tang, Ming & Liao, Huchang & Shen, Wenjing & Lev, Benjamin, 2019. "The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next?," Omega, Elsevier, vol. 87(C), pages 205-225.
    3. Zhiying Zhang & Huchang Liao & Jiaying Chang & Abdullah Al-barakati, 2019. "Green-Building-Material Supplier Selection with a Rough-Set-Enhanced Quality Function Deployment," Sustainability, MDPI, vol. 11(24), pages 1-21, December.
    4. Seok-Keun Yoo & Bo-Young Kim, 2018. "A Decision-Making Model for Adopting a Cloud Computing System," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abhijit Saha & Arunodaya Raj Mishra & Pratibha Rani & Ibrahim M. Hezam & Fausto Cavallaro, 2022. "A q -Rung Orthopair Fuzzy FUCOM Double Normalization-Based Multi-Aggregation Method for Healthcare Waste Treatment Method Selection," Sustainability, MDPI, vol. 14(7), pages 1-28, March.
    2. Manuel Sousa & Maria Fatima Almeida & Rodrigo Calili, 2021. "Multiple Criteria Decision Making for the Achievement of the UN Sustainable Development Goals: A Systematic Literature Review and a Research Agenda," Sustainability, MDPI, vol. 13(8), pages 1-37, April.

    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. Liang, Fuqi & Brunelli, Matteo & Rezaei, Jafar, 2020. "Consistency issues in the best worst method: Measurements and thresholds," Omega, Elsevier, vol. 96(C).
    2. Pushparenu Bhattacharjee & Syed Abou Iltaf Hussain & V. Dey & U. K. Mandal, 2023. "Failure mode and effects analysis for submersible pump component using proportionate risk assessment model: a case study in the power plant of Agartala," 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. 14(5), pages 1778-1798, October.
    3. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).
    4. Zhao, Meng & Xu, Chang & Zhao, Wenxian & Lin, Mingwei, 2023. "New energy vehicle online selection method considering attribute compensation relationship and aspiration strength," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    5. Čábelková, Inna & Strielkowski, Wadim & Streimikiene, Dalia & Cavallaro, Fausto & Streimikis, Justas, 2021. "The social acceptance of nuclear fusion for decision making towards carbon free circular economy: Evidence from Czech Republic," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    6. Besharati Fard, Moein & Moradian, Parisa & Emarati, Mohammadreza & Ebadi, Mehdi & Gholamzadeh Chofreh, Abdoulmohammad & Klemeŝ, Jiří Jaromír, 2022. "Ground-mounted photovoltaic power station site selection and economic analysis based on a hybrid fuzzy best-worst method and geographic information system: A case study Guilan province," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    7. Osama Abied & Othman Ibrahim & Siti Nuur-Ila Mat Kamal & Ibrahim M. Alfadli & Weam M. Binjumah & Norafida Ithnin & Maged Nasser, 2022. "Probing Determinants Affecting Intention to Adopt Cloud Technology in E-Government Systems," Sustainability, MDPI, vol. 14(23), pages 1-29, November.
    8. Xiao-Kang Wang & Wen-Hui Hou & Chao Song & Min-Hui Deng & Yong-Yi Li & Jian-Qiang Wang, 2021. "BW-MaxEnt: A Novel MCDM Method for Limited Knowledge," Mathematics, MDPI, vol. 9(14), pages 1-17, July.
    9. Darell Edmond & Vijay Prakash & Lalit Garg & Seema Bawa, 2022. "Adoption of Cloud Services in Central Banks: Hindering Factors and the Recommendations for Way Forward," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 11(2), pages 123-143.
    10. Akbari, Sina & Escobedo, Adolfo R., 2023. "Beyond kemeny rank aggregation: A parameterizable-penalty framework for robust ranking aggregation with ties," Omega, Elsevier, vol. 119(C).
    11. Chong Li & He Huang & Ya Luo, 2022. "An Integrated Two-Dimension Linguistic Intuitionistic Fuzzy Decision-Making Approach for Unmanned Aerial Vehicle Supplier Selection," Sustainability, MDPI, vol. 14(18), pages 1-24, September.
    12. Sarbast Moslem & Muhammet Gul & Danish Farooq & Erkan Celik & Omid Ghorbanzadeh & Thomas Blaschke, 2020. "An Integrated Approach of Best-Worst Method (BWM) and Triangular Fuzzy Sets for Evaluating Driver Behavior Factors Related to Road Safety," Mathematics, MDPI, vol. 8(3), pages 1-20, March.
    13. Zsombor Szádoczki & Sándor Bozóki & Patrik Juhász & Sergii V. Kadenko & Vitaliy Tsyganok, 2023. "Incomplete pairwise comparison matrices based on graphs with average degree approximately 3," Annals of Operations Research, Springer, vol. 326(2), pages 783-807, July.
    14. Máximo Méndez & Mariano Frutos & Fabio Miguel & Ricardo Aguasca-Colomo, 2020. "TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design Problem," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
    15. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    16. Md. Abdul Moktadir & Ashish Dwivedi & Akib Rahman & Charbel Jose Chiappetta Jabbour & Sanjoy Kumar Paul & Razia Sultana & Jitender Madaan, 2020. "An investigation of key performance indicators for operational excellence towards sustainability in the leather products industry," Business Strategy and the Environment, Wiley Blackwell, vol. 29(8), pages 3331-3351, December.
    17. Gholamreza Haseli & Reza Sheikh & Jianqiang Wang & Hana Tomaskova & Erfan Babaee Tirkolaee, 2021. "A Novel Approach for Group Decision Making Based on the Best–Worst Method (G-BWM): Application to Supply Chain Management," Mathematics, MDPI, vol. 9(16), pages 1-20, August.
    18. Murad, C.A. & Bellinello, M.M. & Silva, A.J. & Netto, A. Caminada & de Souza, G.F.M. & Nabeta, S.I., 2022. "A novel methodology employed for ranking and consolidating performance indicators in holding companies with multiple power plants based on multi-criteria decision-making method," Operations Research Perspectives, Elsevier, vol. 9(C).
    19. Kheybari, Siamak & Javdanmehr, Mahsa & Rezaie, Fariba Mahdi & Rezaei, Jafar, 2021. "Corn cultivation location selection for bioethanol production: An application of BWM and extended PROMETHEE II," Energy, Elsevier, vol. 228(C).
    20. Bai, Chunguang & Zhu, Qingyun & Sarkis, Joseph, 2021. "Joint blockchain service vendor-platform selection using social network relationships: A multi-provider multi-user decision perspective," International Journal of Production Economics, Elsevier, vol. 238(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:gam:jsusta:v:12:y:2020:i:8:p:3410-:d:349060. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.