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    <journal-meta>
      <journal-id journal-id-type="nlm-ta">Rea Press</journal-id>
      <journal-id journal-id-type="publisher-id">null</journal-id>
      <journal-title>Rea Press</journal-title><issn pub-type="ppub">3115-932X</issn><issn pub-type="epub">3115-932X</issn><publisher>
      	<publisher-name>Rea Press</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">https://doi.org/10.48314/anowa.v2i1.66</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group><subject>Workforce optimization, Multi-criteria decision-making, Delphi method, Best–worst method, TOPSIS</subject></subj-group>
      </article-categories>
      <title-group>
        <article-title>A Multi-Criteria Decision-Making Approach to Workforce Optimization in Public Sector Organizations</article-title><subtitle>A Multi-Criteria Decision-Making Approach to Workforce Optimization in Public Sector Organizations</subtitle></title-group>
      <contrib-group><contrib contrib-type="author">
	<name name-style="western">
	<surname> Ghaziani </surname>
		<given-names>Khadijeh</given-names>
	</name>
	<aff>Department of Basic Sciences, Ayandegan University, Tonekabon, Iran.</aff>
	</contrib><contrib contrib-type="author">
	<name name-style="western">
	<surname>Esfandiari </surname>
		<given-names>Mahmoud </given-names>
	</name>
	<aff>Department of Business Management, Ayandegan University, Tonekabon, Iran.</aff>
	</contrib></contrib-group>		
      <pub-date pub-type="ppub">
        <month>03</month>
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>19</day>
        <month>03</month>
        <year>2026</year>
      </pub-date>
      <volume>2</volume>
      <issue>1</issue>
      <permissions>
        <copyright-statement>© 2026 Rea Press</copyright-statement>
        <copyright-year>2026</copyright-year>
        <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.5/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p></license>
      </permissions>
      <related-article related-article-type="companion" vol="2" page="e235" id="RA1" ext-link-type="pmc">
			<article-title>A Multi-Criteria Decision-Making Approach to Workforce Optimization in Public Sector Organizations</article-title>
      </related-article>
	  <abstract abstract-type="toc">
		<p>
			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.
		</p>
		</abstract>
    </article-meta>
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