A Multi-Criteria Decision-Making Approach to Workforce Optimization in Public Sector Organizations

Authors

  • Khadijeh Ghaziani * Department of Basic Sciences, Ayandegan University, Tonekabon, Iran. https://orcid.org/0009-0000-8721-3233
  • Mahmoud Esfandiari Department of Business Management, Ayandegan University, Tonekabon, Iran.

https://doi.org/10.48314/anowa.v2i1.66

Abstract

This study aims to propose strategies for optimizing the workforce in governmental organizations through a Multi-Criteria Decision-Making (MCDM) approach, applied to the Foundation of Martyrs and Veterans Affairs of Mazandaran Province. The research employs a mixed-methods methodology, including a systematic literature review, a three-round Delphi process with 15 experts, the BWM, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The Delphi results identified 41 components across five dimensions: Structural, skill-based, technological, behavioral, and economic. The BWM ranked the skill-based dimension as the most significant, with a weight of 36.2%. The TOPSIS method identified three top-priority strategies: continuous training, talent management, and succession planning. Sensitivity analysis confirmed the robustness of the model. The novelty of this research lies in the development of a hybrid model integrating the Delphi method, the Best–Worst Method, and TOPSIS, applied for the first time within the Foundation of Martyrs. This model enhances decision-making processes by transitioning from experience-based to scientifically grounded approaches. Furthermore, it addresses a knowledge gap in integrated workforce planning in the public sector and, by prioritizing competency-based approaches, increases productivity by 25% to 35%. It is recommended that the organization consider establishing a competency development center, implementing job rotation, and deploying an intelligent dashboard system. The proposed model is suggested as a generalizable framework for veterans’ institutions and other governmental organizations.

Keywords:

Workforce optimization, Multi-criteria decision-making, Delphi method, Best–worst method, TOPSIS

References

  1. [1] Torabi, N. (2022). Investigating factors affecting human resource productivity in government organizations. International journal of current science research and review, 5(1), 44–49. https://doi.org/10.47191/ijcsrr/V5-i1-06
  2. [2] Emami, N. (2018). Designing a multi-purpose fuzzy model for human resource management in production systems. International journal of computer applications, 181(29), 5–11. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.ijcaonline.org/archives/volume181/number29/emami-2018-ijca-918101.pdf
  3. [3] Finn, E. (2018). Workforce optimization and alignment: One school’s approach to organizational design. Strategic enrollment management quarterly, 5(6), 165–174. https://search.proquest.com/openview/f5784520c4ea9cc4be5a7bae2aa1ee64/1?pq-origsite=gscholar&cbl=5324452
  4. [4] Nazmfat, H., & Alibakhshi, A. (2018). Compare the efficiency of multi-criteria decision-making methods In order to determine the level of development of the county. Geography and regional planning, 8(30), 147-166. (In Persian). https://dor.isc.ac/dor/20.1001.1.22286462.1397.8.2.10.9
  5. [5] Cherna, T., & Shestakevych, T. (2025). Optimization and post-optimization analysis of the personnel structure of local self-government bodies to improve the reintegration of veterans. 2nd international conference on smart automation & robotics for future industry. Smart Industry (pp. 310–316). chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://ceur-ws.org/Vol-3970/SHORT8.pdf
  6. [6] Mohammadi, A., Shojaee, P., Akbari, Z., & Kayedan, B. (2016). Comparative analysis multi criteria decision making approach in prioritizing provinces based on civil projects. Quarterly journal of the macro and strategic policies, 3(12), 27-50. (In Persian). https://www.jmsp.ir/article_12947.html
  7. [7] Alshamsi, A. M., El-Kassabi, H., Serhani, M. A., & Bouhaddioui, C. (2023). A multi-criteria decision-making (MCDM) approach for data-driven distance learning recommendations. Education and information technologies, 28(8), 10421–10458. https://doi.org/10.1007/s10639-023-11589-9
  8. [8] Tohidinia, A., Azar, A., & Ahmadi, M. (2023). The suitable pattern of financing supply chain of Iran’s defense industry. Journal of logistics thought scientific publication, 22(86), 135-161. (In Persian). https://sid.ir/paper/1116569/fa
  9. [9] Nikouei, M. A., & AMIRI, M. (2022). Application of experimental design methods and fuzzy multi-criteria decision making in the determination of the optimal reliability of the two-stage assembly flow shop scheduling system. Journal of operational research in its applications, 19(3), 45-62. (In Persian). https://www.sid.ir/paper/1083708
  10. [10] Izadi, E., & Koushki, H. (2018). Identifying and analyzing major indicators in the field of conceptually upgrading skills training to the skills of the national education movement in Iran using the AHP method. National technical and vocational education. (In Persian). chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://ensani.ir/file/download/article/1644127243-10514-1400-166.pdf
  11. [11] Ozdogan, S., Yildizbasi, A., & Rouyendegh, B. D. (2020). Performance evaluation of municipal services with fuzzy multi-criteria decision making approaches: a case study from Turkey. SN applied sciences, 2(6), 1056. https://doi.org/10.1007/s42452-020-2843-8
  12. [12] Giotopoulos, K. C., Michalopoulos, D., Vonitsanos, G., Papadopoulos, D., Giannoukou, I., & Sioutas, S. (2025). Dynamic workload management system in the public sector: A comparative analysis. Future internet, 17(3), 119. https://doi.org/10.3390/fi17030119
  13. [13] Wang, Z., Luo, Z., & Shen, H. (2025). Data-driven robust flexible personnel scheduling. Computers & operations research, 176, 106935. https://doi.org/10.1016/j.cor.2024.106935
  14. [14] Fuchs, G., Schimmelpfeng, K., & Brunner, J. O. (2025). A roadmap for integrating fairness in personnel planning and scheduling in hospitals. Operations research, data analytics and logistics, 45, 200479. https://doi.org/10.1016/j.ordal.2025.200479
  15. [15] Bas, M. C., Bolós, V. J., Prieto, Á. E., Rodríguez-Echeverría, R., & Sánchez-Figueroa, F. (2024). A multi-criteria decision support system to evaluate the effectiveness of training courses on citizens’ employability. Applied intelligence, 55(1), 57. https://doi.org/10.1007/s10489-024-05967-0
  16. [16] Böðvarsdóttir, E. B., & Stidsen, T. (2026). A review of multi-objective optimization methods for personnel rostering problems. Journal of scheduling, 29(1), 1–19. https://doi.org/10.1007/s10951-025-00845-0
  17. [17] Patel, V., Deodhar, A., & Birru, D. (2025). A multi-objective genetic algorithm for healthcare workforce scheduling. ArXiv. https://doi.org/10.48550/arXiv.2508.20953
  18. [18] Nasirian, A., Zhang, L., Costa, A., & Abbasi, B. (2024). Multiskilled workforce staffing and scheduling: A logic-based Benders’ decomposition approach. European journal of operational research, 323(1), 20–33. https://doi.org/10.1016/j.ejor.2024.11.033
  19. [19] Brodny, J., & Tutak, M. (2024). A multi-criteria measurement and assessment of human capital development in EU-27 countries: A 10-year perspective. Journal of open innovation: Technology, market, and complexity, 10(4), 100394. https://doi.org/10.1016/j.joitmc.2024.100394
  20. [20] Corral, F., Forcael, E., & Linfati, R. (2023). Workforce scheduling efficiency assessment in construction projects through a multi-objective optimization model in the COVID-19 context. Heliyon, 9(6). https://doi.org/10.1016/j.heliyon.2023.e16745
  21. [21] Mansini, R., Zanella, M., & Zanotti, R. (2023). Optimizing a complex multi-objective personnel scheduling problem jointly complying with requests from customers and staff. Omega, 114, 102722. https://doi.org/10.1016/j.omega.2022.102722
  22. [22] Guastaroba, G., Côté, J.-F., & Coelho, L. C. (2021). The multi-period workforce scheduling and routing problem. Omega, 102(C), 102302. https://doi.org/10.1016/j.omega.2020.102302
  23. [23] Moussa Pourmamodan, M. (2013). Development of a data-driven analysis model for human resource planning and optimization [Thesis]. (In Persian).
  24. [24] Nemadez Ebrahimpour, Sh. (1400). Designing an optimal control model for workforce planning based on the human skills required for the health sector workforce [Thesis]. (In Persian).
  25. [25] Kargar, Gh. (2009). Human resources optimization as a solution for sustainable organization development: analytical study of factors and solutions. Police human development, 6(27), 65-83. (In Persian). https://sid.ir/paper/132809/fa
  26. [26] Mehraban, F., Ghorbani, M., & Erfanian Khanzadeh, H. (2023). The model design of human resources development with the approach of individual self-development in education and upbringing in Mashhad. Journal-of-psychological-science, 22(129), 267-285. (In Persian). http://dx.doi.org/10.52547/JPS.22.129.267
  27. [27] Vakili, S., Salavati, A., & Sahabi, J. (2024). Investigating the development of a model for maximizing decision implementation in public organizations based on the expansion of the zone of indifference among executives using a qualitative method (Case study: Iran trade promotion organization). Journal of personal development and organizational transformation, 2(4), 113-128. (In Persian). https://doi.org/10.61838/kman.jpdot.2.4.8
  28. [28] Afshari, D., Shirali, G. A., & Shabgard, Z. (2024). Prediction and prioritization of individual and organizational factors affecting turnover intention based on fuzzy Delphi and analytical hierarchy process (AHP) method: A case study of employees of a health center in Ahvaz city in Iran. Umsha-johe, 11(2), 144-156. (In Persian). https://doi.org/10.32592/joohe.11.2.144
  29. [29] Raisi M. H., & Nik Seresht A. (2012). Using the VIKOR multi-criteria decision making model to evaluate and prioritize the human resources capability of different departments of Tehran municipality. Scientific journal of new research approaches to manageme, 74(3), 1-17. (In Persian). https://majournal.ir/index.php/ma/article/view/955/774
  30. [30] Qashqaizadeh, N. (2020). Design and development of a model for improving the quality of working life of faculty members. Iranian occupational health. (In Persian). https://search.isc.ac/dl/search/defaultta.aspx?DTC=8&DC=1172786
  31. [31] Kiani, N., &Radfar, R. (2015). Identifying and ranking factors affecting organizational productivity using the Dematel model. Productivity management (beyond management), 9(35), 111-130. (In Persian). https://sid.ir/paper/181865/
  32. [32] D’Urso, M. G., & Masi, D. (2015). Multi-criteria decision-making methods and their applications for human resources. The international archives of the photogrammetry, remote sensing and spatial information sciences, 40, 31–37. https://doi.org/10.5194/isprsarchives-XL-6-W1-31-2015
  33. [33] Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. New York: McGraw-Hill. https://books.google.com/books/about/The_Analytic_Hierarchy_Process.html?id=Z8jHAAAAMAAJ
  34. [34] Hwang, C.-L., & Yoon, K. (2012). Multiple attribute decision making: Methods and applications a state-of-the-art survey. Springer Science & Business Media. https://books.google.com/books?id=7yr6CAAAQBAJ&source=gbs_navlinks_s
  35. [35] Saaty, T. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83–98. https://doi.org/10.1504/IJSSCI.2008.017590
  36. [36] Dalkey, N., & Helmer, O. (1963). An experimental application of the DELPHI method to the use of experts. Management science, 9(3), 458–467. https://doi.org/10.1287/mnsc.9.3.458
  37. [37] Linstone, H. A., Turoff, M., & others. (1975). The Delphi method. Addison-wesley reading. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://d1wqtxts1xzle7.cloudfront.net/29694542/delphibook.pdf?1738024017=&response-content-disposition=inline%3B+filename%3DThe_Delphi_Method.pdf&Expires=1781214311&Signature=bcMf.
  38. [38] Okoli, C., & Pawlowski, S. D. (2004). The Delphi method as a research tool: an example, design considerations and applications. Information & management, 42(1), 15–29. https://doi.org/10.1016/j.im.2003.11.002
  39. [39] Rowe, G., Wright, G., & Bolger, F. (1991). Delphi: A reevaluation of research and theory. Technological forecasting and social change, 39(3), 235–251. https://doi.org/10.1016/0040-1625(91)90039-I
  40. [40] Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009
  41. [41] Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001
  42. [42] Treiblmaier, H., & Petrozhitskaya, E. (2023). Is it time for marketing to reappraise B2C relationship management? The emergence of a new loyalty paradigm through blockchain technology. Journal of business research, 159, 113725. https://doi.org/10.1016/j.jbusres.2023.113725
  43. [43] Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-based systems, 121, 23–31. https://doi.org/10.1016/j.knosys.2017.01.010
  44. [44] Hwang, F. P., Chen, S. J., & Hwang, C. L. (2012). Fuzzy multiple attribute decision making: Methods and applications. Springer Berlin Heidelberg. https://books.google.com/books?id=yXH1CAAAQBAJ
  45. [45] Lee, S. Y., & Hong, A. J. (2021). Psychometric investigation of the cultural intelligence scale using the rasch measurement model in South Korea. Sustainability, 13(6), 3139. https://doi.org/10.3390/su13063139
  46. [46] Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European journal of operational research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
  47. [47] Asadabadi, M. R., Ahmadi, H. B., Gupta, H., & Liou, J. J. H. (2023). Supplier selection to support environmental sustainability: the stratified BWM TOPSIS method. Annals of operations research, 322(1), 321–344. https://doi.org/10.1007/s10479-022-04878-y
  48. [48] Pohan, A. B., Hadi, S. W., Rahmatullah, S., Zuama, R. A., Rifai, A., & Gunawan, D. (2021). Employee performance apparaisal using decision support system by AHP and TOPSIS methods. AMIK bsi computer engineering journal, 7(1), 100–105. 10.31294/jtk.v4i2

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Published

2026-03-18

Issue

Vol. 2 No. 1 (2026): Annals of Optimization With Applications (ANOWA)

Section

Articles

How to Cite

Ghaziani, K. ., & Esfandiari, M. . (2026). A Multi-Criteria Decision-Making Approach to Workforce Optimization in Public Sector Organizations. Annals of Optimization With Applications, 2(1), 48-74. https://doi.org/10.48314/anowa.v2i1.66

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