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Hierarchical fuzzy DEA model with double frontiers combined with TOPSIS technique: application on mobile money agents locations

Author

Listed:
  • Jacob Muvingi

    (Harare Institute of Technology
    University of Technology, Mauritius)

  • Arshad Ahmud Iqbal Peer

    (University of Technology, Mauritius)

  • Josef Jablonský

    (Prague University of Economics and Business)

  • Hossein Azizi

    (Islamic Azad University)

  • Farhad Hosseinzadeh Lotfi

    (Islamic Azad University)

Abstract

Hierarchical data envelopment analysis (DEA) models evaluate the efficiency of decision-making units (DMUs) with a hierarchical group structure, where sub-DMUs are grouped into main DMUs. Contrary to early hierarchical DEA models, which were based on crisp input and output data, the model adopted in this study considers fuzziness attributes in the input and output data. The efficiency analysis was mainly based on the optimistic frontier in the early conventional fuzzy DEA models. To get a full view of the efficiency of the DMUs, we incorporated a second frontier based on the pessimistic assumption. In this study, two levels of analysis were implemented for the sub-DMUs and main DMUs. A combination of the optimistic and pessimistic efficiency ratings for both levels of analysis was done to get the overall performance measures. We applied the proposed approach to analyse the mobile money agents’ locations. Aggregating sub-DMUs with ratings of the main DMUs improved the segregation of sub-DMUs in different groups. However, within groups, most sub-DMUs had similar ratings, making it difficult to distinguish the performance of the DMUs. To further improve the segregation of the DMUs, we applied the technique for order preference by similarity to the ideal solution (TOPSIS) based on the overall performance measures. The performance of 75% of the sub-DMUs was completely segregated, whilst 100% was achieved for the main DMUs. The final TOPSIS results were validated using the principal component analysis (PCA). A Spearman correlation of 50% was observed between the TOPSIS and the PCA results. The accuracy of the district location TOPSIS rankings on the final suburb location TOPSIS rankings was 72%.

Suggested Citation

  • Jacob Muvingi & Arshad Ahmud Iqbal Peer & Josef Jablonský & Hossein Azizi & Farhad Hosseinzadeh Lotfi, 2024. "Hierarchical fuzzy DEA model with double frontiers combined with TOPSIS technique: application on mobile money agents locations," OPSEARCH, Springer;Operational Research Society of India, vol. 61(3), pages 1154-1191, September.
  • Handle: RePEc:spr:opsear:v:61:y:2024:i:3:d:10.1007_s12597-023-00734-0
    DOI: 10.1007/s12597-023-00734-0
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    References listed on IDEAS

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    1. Adel Hatami-Marbini & Saber Saati & Seyed Mojtaba Sajadi, 2018. "Efficiency analysis in two-stage structures using fuzzy data envelopment analysis," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(4), pages 909-932, December.
    2. Azadeh, Ali & Rahimi-Golkhandan, Armin & Moghaddam, Mohsen, 2014. "Location optimization of wind power generation–transmission systems under uncertainty using hierarchical fuzzy DEA: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 877-885.
    3. Hatami-Marbini, Adel & Emrouznejad, Ali & Tavana, Madjid, 2011. "A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making," European Journal of Operational Research, Elsevier, vol. 214(3), pages 457-472, November.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Thanh Tam Le & Nguyen Dieu Linh Dang & Thi Dieu Thu Nguyen & Thanh Son Vu & Manh Dung Tran, 2019. "Determinants of Financial Inclusion: Comparative Study of Asian Countries," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(10), pages 1107-1123.
    6. A. Charnes & W. W. Cooper, 1962. "Programming with linear fractional functionals," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 9(3‐4), pages 181-186, September.
    7. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    8. Jacob Muvingi & Arshad Ahmud Iqbal Peer & Farhad Hosseinzadeh Lotfi & Volkan Soner Özsoy, 2022. "Hierarchical groups DEA cross-efficiency and TOPSIS technique: an application on mobile money agents locations," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 21(2), pages 171-199.
    9. Abubakar Adamu Magaji & Daneji Bashir Ahmad & Muhammed Ahmed Ibrahim & Chekene Imam-Ahmad Buba, 2020. "Driving faster financial inclusion in developing nations," Technology audit and production reserves, Socionet;Technology audit and production reserves, vol. 2(4(52)), pages 35-40.
    10. Thanh Tam Le & Nguyen Dieu Linh Dang & Thi Dieu Thu Nguyen & Thanh Son Vu & Manh Dung Tran, 2019. "Determinants of Financial Inclusion: Comparative Study of Asian Countries," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(10), pages 1107-1123, October.
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    More about this item

    Keywords

    Hierarchical models; Data envelopment analysis; Double frontiers; TOPSIS;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models

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