IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i5p745-d355452.html
   My bibliography  Save this article

A Model for Determining Weight Coefficients by Forming a Non-Decreasing Series at Criteria Significance Levels (NDSL)

Author

Listed:
  • Mališa Žižović

    (Faculty of Technical Sciences in Cacak, University of Kragujevac, Svetog Save 65, 32102 Cacak, Serbia)

  • Dragan Pamučar

    (Department of Logistics, Military Academy, University of Defence, Pavla Jurisica Sturma 33, 11000 Belgrade, Serbia)

  • Goran Ćirović

    (Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia)

  • Miodrag M. Žižović

    (AXIS Translations and Technical Services, 11000 Belgrade, Serbia)

  • Boža D. Miljković

    (Faculty of Education Sombor, University of Novi Sad, 21000 Novi Sad, Serbia)

Abstract

In this paper, a new method for determining weight coefficients by forming a non-decreasing series at criteria significance levels (the NDSL method) is presented. The NDLS method includes the identification of the best criterion (i.e., the most significant and most influential criterion) and the ranking of criteria in a decreasing series from the most significant to the least significant criterion. Criteria are then grouped as per the levels of significance within the framework of which experts express their preferences in compliance with the significance of such criteria. By employing this procedure, fully consistent results are obtained. In this paper, the advantages of the NDSL model are singled out through a comparison with the Best Worst Method (BWM) and Analytic Hierarchy Process (AHP) models. The advantages include the following: (1) the NDSL model requires a significantly smaller number of pairwise comparisons of criteria, only involving an n − 1 comparison, whereas the AHP requires an n ( n − 1)/2 comparison and the BWM a 2 n − 3 comparison; (2) it enables us to obtain reliable (consistent) results, even in the case of a larger number of criteria (more than nine criteria); (3) the NDSL model applies an original algorithm for grouping criteria according to the levels of significance, through which the deficiencies of the 9-degree scale applied in the BWM and AHP models are eliminated. By doing so, the small range and inconsistency of the 9-degree scale are eliminated; (4) while the BWM includes the defining of one unique best/worst criterion, the NDSL model eliminates this limitation and gives decision-makers the freedom to express the relationships between criteria in accordance with their preferences. In order to demonstrate the performance of the developed model, it was tested on a real-world problem and the results were validated through a comparison with the BWM and AHP models.

Suggested Citation

  • Mališa Žižović & Dragan Pamučar & Goran Ćirović & Miodrag M. Žižović & Boža D. Miljković, 2020. "A Model for Determining Weight Coefficients by Forming a Non-Decreasing Series at Criteria Significance Levels (NDSL)," Mathematics, MDPI, vol. 8(5), pages 1-18, May.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:5:p:745-:d:355452
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/5/745/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/5/745/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gupta, Himanshu & Barua, Mukesh Kumar, 2016. "Identifying enablers of technological innovation for Indian MSMEs using best–worst multi criteria decision making method," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 69-79.
    2. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2007. "Extended VIKOR method in comparison with outranking methods," European Journal of Operational Research, Elsevier, vol. 178(2), pages 514-529, April.
    3. Professor Snezana UROSEVIC & Darjan KARABASEVIC & Dragisa STANUJKIC & Mladjan MAKSIMOVIC, 2017. "An Approach to Personnel Selection in the Tourism Industry Based on the SWARA and the WASPAS Methods," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(1), pages 75-88.
    4. Geerten Van de Kaa & Daniel Scholten & Jafar Rezaei & Christine Milchram, 2017. "The Battle between Battery and Fuel Cell Powered Electric Vehicles: A BWM Approach," Energies, MDPI, vol. 10(11), pages 1-13, October.
    5. Edwards, Ward & Barron, F. Hutton, 1994. "SMARTS and SMARTER: Improved Simple Methods for Multiattribute Utility Measurement," Organizational Behavior and Human Decision Processes, Elsevier, vol. 60(3), pages 306-325, December.
    6. Negin Salimi & Jafar Rezaei, 2016. "Measuring efficiency of university-industry Ph.D. projects using best worst method," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1911-1938, December.
    7. Negin Salimi, 2017. "Quality assessment of scientific outputs using the BWM," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 195-213, July.
    8. Peipei You & Sen Guo & Haoran Zhao & Huiru Zhao, 2017. "Operation Performance Evaluation of Power Grid Enterprise Using a Hybrid BWM-TOPSIS Method," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
    9. Rezaei, Jafar & Hemmes, Alexander & Tavasszy, Lori, 2017. "Multi-criteria decision-making for complex bundling configurations in surface transportation of air freight," Journal of Air Transport Management, Elsevier, vol. 61(C), pages 95-105.
    10. Ren, Jingzheng & Liang, Hanwei & Chan, Felix T.S., 2017. "Urban sewage sludge, sustainability, and transition for Eco-City: Multi-criteria sustainability assessment of technologies based on best-worst method," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 29-39.
    11. Belton, Valerie, 1986. "A comparison of the analytic hierarchy process and a simple multi-attribute value function," European Journal of Operational Research, Elsevier, vol. 26(1), pages 7-21, July.
    12. Rezaei, Jafar, 2016. "Best-worst multi-criteria decision-making method: Some properties and a linear model," Omega, Elsevier, vol. 64(C), pages 126-130.
    13. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    14. Zanakis, Stelios H. & Solomon, Anthony & Wishart, Nicole & Dublish, Sandipa, 1998. "Multi-attribute decision making: A simulation comparison of select methods," European Journal of Operational Research, Elsevier, vol. 107(3), pages 507-529, June.
    15. Tzeng, Gwo-Hshiung & Chen, Ting-Yu & Wang, Jih-Chang, 1998. "A weight-assessing method with habitual domains," European Journal of Operational Research, Elsevier, vol. 110(2), pages 342-367, October.
    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. 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.
    2. Mohammadi, Majid & Rezaei, Jafar, 2020. "Bayesian best-worst method: A probabilistic group decision making model," Omega, Elsevier, vol. 96(C).
    3. Salimi, Negin & Rezaei, Jafar, 2018. "Evaluating firms’ R&D performance using best worst method," Evaluation and Program Planning, Elsevier, vol. 66(C), pages 147-155.
    4. Javid Nafari & Alireza Arab & Sina Ghaffari, 2017. "Through the Looking Glass: Analysis of Factors Influencing Iranian Student’s Study Abroad Motivations and Destination Choice," SAGE Open, , vol. 7(2), pages 21582440177, June.
    5. Geerten Van de Kaa & Daniel Scholten & Jafar Rezaei & Christine Milchram, 2017. "The Battle between Battery and Fuel Cell Powered Electric Vehicles: A BWM Approach," Energies, MDPI, vol. 10(11), pages 1-13, October.
    6. Huseyin Kocak & Atalay Caglar & Gulin Zeynep Oztas, 2018. "Euclidean Best–Worst Method and Its Application," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(05), pages 1587-1605, September.
    7. Shih-Chia Chang & Ming-Tsang Lu & Mei-Jen Chen & Li-Hua Huang, 2021. "Evaluating the Application of CSR in the High-Tech Industry during the COVID-19 Pandemic," Mathematics, MDPI, vol. 9(15), pages 1-16, July.
    8. Shojaei, Payam & Seyed Haeri, Seyed Amin & Mohammadi, Sahar, 2018. "Airports evaluation and ranking model using Taguchi loss function, best-worst method and VIKOR technique," Journal of Air Transport Management, Elsevier, vol. 68(C), pages 4-13.
    9. Dragan Pamučar & Fatih Ecer & Goran Cirovic & Melfi A. Arlasheedi, 2020. "Application of Improved Best Worst Method (BWM) in Real-World Problems," Mathematics, MDPI, vol. 8(8), pages 1-19, August.
    10. van de Kaa, G. & Fens, T. & Rezaei, J. & Kaynak, D. & Hatun, Z. & Tsilimeni-Archangelidi, A., 2019. "Realizing smart meter connectivity: Analyzing the competing technologies Power line communication, mobile telephony, and radio frequency using the best worst method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 320-327.
    11. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    12. Haoran Zhao & Huiru Zhao & Sen Guo, 2018. "Comprehensive Performance Evaluation of Electricity Grid Corporations Employing a Novel MCDM Model," Sustainability, MDPI, vol. 10(7), pages 1-23, June.
    13. Gupta, Himanshu, 2018. "Evaluating service quality of airline industry using hybrid best worst method and VIKOR," Journal of Air Transport Management, Elsevier, vol. 68(C), pages 35-47.
    14. Peipei You & Sen Guo & Haoran Zhao & Huiru Zhao, 2017. "Operation Performance Evaluation of Power Grid Enterprise Using a Hybrid BWM-TOPSIS Method," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
    15. Badri Ahmadi, Hadi & Kusi-Sarpong, Simonov & Rezaei, Jafar, 2017. "Assessing the social sustainability of supply chains using Best Worst Method," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 99-106.
    16. van de Kaa, Geerten & Janssen, Marijn & Rezaei, Jafar, 2018. "Standards battles for business-to-government data exchange: Identifying success factors for standard dominance using the Best Worst Method," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 182-189.
    17. Pei-Yao Su & Jing-Hong Guo & Qi-Gan Shao, 2021. "Construction of the Quality Evaluation Index System of MOOC Platforms Based on the User Perspective," Sustainability, MDPI, vol. 13(20), pages 1-18, October.
    18. Željko Stević & Irena Đalić & Dragan Pamučar & Zdravko Nunić & Slavko Vesković & Marko Vasiljević & Ilija Tanackov, 2019. "A new hybrid model for quality assessment of scientific conferences based on Rough BWM and SERVQUAL," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 1-30, April.
    19. Hamzeh Soltanali & Mehdi Khojastehpour & Siamak Kheybari, 2023. "Evaluating the critical success factors for maintenance management in agro-industries using multi-criteria decision-making techniques," Operations Management Research, Springer, vol. 16(2), pages 949-968, June.
    20. Omidipoor, Morteza & Jelokhani-Niaraki, Mohammadreza & Moeinmehr, Athena & Sadeghi-Niaraki, Abolghasem & Choi, Soo-Mi, 2019. "A GIS-based decision support system for facilitating participatory urban renewal process," Land Use Policy, Elsevier, vol. 88(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:jmathe:v:8:y:2020:i:5:p:745-:d:355452. 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.