<![CDATA[This study presents a theoretical and methodological advancement of the Hungarian maximization model for optimizing multi-site human resource allocation. Traditional Hungarian algorithms focus on single-site, cost-minimization assignments, limiting their applicability in modern workforce environments characterized by distributed operations and diverse employee attributes. To address these gaps, the study reformulates the classical objective function into a maximization framework and incorporates multi-site constraints, multi-criteria employee attributes, and workload balancing requirements. The enhanced model is evaluated through mathematical analysis and simulation-based case studies to assess its performance relative to baseline assignment and heuristic optimization methods. The results demonstrate that the proposed model achieves higher organizational productivity, reduces operational costs, improves staff distribution equity, and significantly accelerates computation time compared with existing approaches. Moreover, the model ensures more consistent alignment between employee capabilities and site-level demands, offering a more robust foundation for strategic workforce deployment. Comparisons with previous studies show that this research provides the first Hungarian-based maximization framework specifically tailored for multi-site HR allocation, overcoming key limitations related to scalability, fairness, and optimality. Overall, this study contributes a rigorous theoretical extension of the Hungarian method and offers practical implications for workforce scheduling, supply-chain staffing, healthcare deployment, and emergency response operations. The findings underscore the potential of deterministic optimization models to support intelligent and equitable human resource decision-making in increasingly complex organizational settings.]]>
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