Optimization & Operations Research
Javad Gerami; Alireza Davoodi
Abstract
Two-stage network Data Envelopment Analysis (DEA) models under variable returns to scale (VRS) suffer from a well-known pitfall: efficiency score decomposition and frontier projection can be mutually inconsistent, undermining both the theoretical foundations and practical interpretability of the results. ...
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Two-stage network Data Envelopment Analysis (DEA) models under variable returns to scale (VRS) suffer from a well-known pitfall: efficiency score decomposition and frontier projection can be mutually inconsistent, undermining both the theoretical foundations and practical interpretability of the results. A further limitation is the universal assumption of strong disposability for all inputs and outputs, which is unrealistic when variables are structurally or statistically interdependent—as is common in healthcare settings. This paper addresses both issues simultaneously by developing a novel two-stage network DEA model under hybrid disposability (HD) technology, which allows selective strong or weak disposability for subsets of closely related inputs, intermediate measures, and outputs. We formally derive the efficiency decomposition and frontier projection under HD technology, establish theoretical consistency between the envelopment and multiplier forms, and prove that the proposed model yields Pareto-efficient targets. The model captures synergistic scale effects across stages and preserves structural dependencies between them, thereby providing a more realistic representation of multi-stage production systems. The practical relevance and advantages of the proposed framework are demonstrated through an empirical case study involving 32 Iranian healthcare centers operating under a two-stage network structure with interdependent variables.
Optimization & Operations Research
Mohammad Eshaghnezhad; Niloufar Kamkar; Javad Banihassan; Jalal A. Nasiri; Amin Mansoori
Abstract
Data envelopment analysis (DEA) is a widely used non-parametric method for evaluating the relative efficiency of peer decision-making units (DMUs). To address the computational demands of conventional solvers, this study develops a neurodynamic optimization framework derived from the Karush--Kuhn--Tucker ...
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Data envelopment analysis (DEA) is a widely used non-parametric method for evaluating the relative efficiency of peer decision-making units (DMUs). To address the computational demands of conventional solvers, this study develops a neurodynamic optimization framework derived from the Karush--Kuhn--Tucker (KKT) optimality conditions of the CCR-based, input-oriented DEA model. To date, only a few studies have applied neurodynamic approaches to DEA, and all of them rely on double-layer structures. Our primary contribution is the construction of a KKT-based dynamic system tailored to the CCR-based DEA formulation. Furthermore, we provide a rigorous theoretical foundation by establishing the Lyapunov stability of the proposed system, thereby ensuring its global convergence to an exact optimal solution. The model's practical effectiveness is validated through a comprehensive case study analyzing the efficiency of participating nations in the Asian Games. The results, obtained using our neurodynamic framework and evaluated in terms of CPU time, demonstrate its robustness and computational efficiency for real-world performance benchmarking.
Optimization & Operations Research
Zahra Mohammadhashemi; Khatere Ghorbani-Moghadam; Safora Allahy; Sepehr Ghazinoory
Abstract
This study employs a two-stage analytical framework to assess efficiency, comprising a standard SBM evaluation and a novel weighted SBM model. Unlike conventional SBM-DEA applications, the proposed weighted model uses an enhanced slack-based mechanism that prioritizes strategic inputs (R&D investment, ...
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This study employs a two-stage analytical framework to assess efficiency, comprising a standard SBM evaluation and a novel weighted SBM model. Unlike conventional SBM-DEA applications, the proposed weighted model uses an enhanced slack-based mechanism that prioritizes strategic inputs (R&D investment, number of employees, and funding) and clearly distinguishes input redundancies (e.g., excessive R&D expenditure or staffing) from output deficiencies (e.g., weak revenue performance). This separation yields more precise and targeted diagnostic insights. Additionally, the model incorporates sector-specific efficiency differentiation, supported by ANOVA, enabling assessment of cross-firm inefficiencies and their statistical significance in terms of systemic versus sector-specific phenomena. The methodology is applied to a distinctive panel of 146 technology-based firms (TBFs) in Iranian science and technology parks from 2021–2023, a context rarely explored with DEA in emerging markets. The study combines quantitative DEA results from both models with qualitative follow-up analyses of factors such as marketing strategies, private investment initiatives, and certification achievements, producing a robust mixed-methods approach and actionable policy recommendations. A comparative analysis reveals that fully efficient firms comprise 2.7\% under the unweighted model and 3.4\% under the weighted model, indicating that weighting yields a small, non-significant change in overall efficiency. About 97.3\% of firms display efficiency gaps due to input redundancies or output shortfalls. Sectoral tests show no statistically significant inter-sector differences, pointing to systemic inefficiencies across industries. Qualitative insights identify firm-level success factors—effective marketing, certification, and investment strategies—that align with the detected inefficiency patterns. Collectively, these findings offer measurable strategies for improvement, such as reducing redundant investment and enhancing revenue-generation mechanisms, to inform evidence-based policy aimed at the commercialization and growth of TBFs in emerging markets.
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