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Showing 226 results for Type of Study: Original
F. Ahmadi, S. Kordrostami, M. Mirzaei Chalakei, L. Khoshandam,
Volume 17, Issue 1 (5-2026)
Abstract
Measuring capacity utilization is an important aspect among processes in order to determine overcapacity or undercapacity. Furthermore, in many situations, the convexity and homogeneity properties are not satisfied. Accordingly, in this paper, meta-frontier free disposal hull (FDH) frameworks are proposed to estimate output-oriented and input-oriented capacity utilization (CU) rates of firms under nonconvexity property and heterogeneity of technology. Also, the introduced technique is applied to assess capacity utilization of some Iranian hospitals. The findings show the presented approach is beneficial to measure capacity utilization rates of systems in the presence of nonconvexity and heterogeneity.
Mobin Sabbaghi Rostami, Saber Khandan Alamdari, Mohammad Hadi Asgari,
Volume 17, Issue 1 (5-2026)
Abstract
Objective: Given global reports of 92% failure of blockchain projects, this study aims to quantitatively validate the challenges of adopting this technology in the Iranian supply chain. The main research question is to explain why, despite its many theoretical attractions, Iranian organizations have failed to move from theoretical discussions to operational implementation and where the roots of this failure lie.
Methodology: This study was conducted with a quantitative approach and survey strategy. The required data were collected through a researcher-made questionnaire with a Likert scale and cluster sampling method from experts and activists in the supply chain field. The validity of the measurement tool was assessed using confirmatory factor analysis and its reliability was assessed by calculating Cronbach's alpha coefficient. In the inferential statistics section, structural equation modeling (SEM) was used to validate the conceptual model of the research, and a single-sample t-test was used to examine the current status of each of the components under study in the statistical population.
Findings: The results of confirmatory factor analysis showed that "legal and legal barriers" with a factor loading of 0.64 play the most decisive role in the formation of acceptance challenges. However, the analysis of the current situation showed that "organizational and cultural factors" with a mean of 2.125 and a negative t-statistic (-3.25) are considered the most critical weakness and main bottleneck in the Iranian supply chain. The findings indicate that while "perceived benefits" are at the highest level, severe weakness in organizational culture and professional readiness prevents this positive perception from being converted into implementation.
Conclusion: The findings of this study explain that the root of the high failure rate of blockchain projects lies not in technical flaws or lack of perception of usefulness, but in the deep gap between the perception of benefits and the incompetence of the organizational structure. For senior managers, these results mean that investing solely in hardware infrastructure without rebuilding organizational culture and specialized training of employees will lead to the inevitable failure of projects. It is suggested that organizations, before entering the technical implementation phase, develop change management strategies to reduce employee resistance and improve technical knowledge at different organizational levels.
Dr Seyed Morteza Mousavi, Dr Javad Gerami, Dr Mohammadreza Mozaffari, Dr Roya M. Pour Ahari, Dr Mohammadreza Feylizadeh,
Volume 17, Issue 2 (9-2026)
Abstract
Evaluating the eco-efficiency of multi-plant petrochemical clusters operating under stringent regional environmental caps requires robust analytical frameworks capable of handling complex operational complexities and parameter ambiguity. Traditional Data Envelopment Analysis (DEA) models often fail to capture systemic fixed-sum undesirable output reallocation constraints and assume deterministic input-output parameters, leading to biased and overly optimistic performance benchmarks. To overcome these limitations, this study develops a novel hybrid predictive-optimization framework integrating Extreme Gradient Boosting (XGBoost) machine learning with a Robust Fuzzy Fixed-Sum Output Data Envelopment Analysis (R-FSODEA) model. The proposed approach utilizes an XGBoost regressor trained on multi-period operational data across 75 major petrochemical complexes to project baseline expectations and establish data-driven symmetric uncertainty bounds (xij,yrj,ykju ) based on out-of-sample prediction residuals. To address both epistemic fuzzy uncertainty and aleatory operational noise, fuzzy parameters are discretized via α -cuts (α∈(0,1] ), while cardinality-constrained uncertainty budgets (Γ ) govern the level of decision conservatism. Multiple interdependent undesirable environmental emissions (CO2 , NOx , SO2 , PM10 , and COD ) are simultaneously constrained under strict aggregate regional fixed-sum emission ceilings (ykju=Cku ). The empirical results reveal a dynamic trade-off between operational conservatism and systemic performance: as the uncertainty budget increases from nominal (Γ=0 ) to fully robust configurations (Γ=4 ), mean industry eco-efficiency monotonically decreases from 0.8514 to 0.6524 , representing the analytical efficiency penalty required to guarantee feasibility under bad-case data perturbations. Furthermore, statistical correlation analyses (Spearman's ρ>0.908 , Kendall's τ>0.907 ) confirm that while individual complexes undergo localized rank shifts due to operational variance sensitivity, the global efficiency hierarchy remains highly stable. The proposed framework equips industrial executives and environmental regulators with an intelligent, policy-relevant decision-support tool for dynamic emission quota allocation, targeted technical retrofits, and eco-efficiency benchmarking under operational parameter uncertainty.
Forouzesh Ghambari, Mahdi Ahangari,
Volume 17, Issue 2 (9-2026)
Abstract
Cooperative game theory is an important tool for analyzing cooperation and benefit allocation among coalition members, but classical models usually do not consider the existence of a minimum capacity required for cooperation to be activated. In this study, “threshold coalition games” are introduced, in which the formation of an effective coalition requires crossing a certain threshold of collective capacity. Also, a coalition activation index and a new rule called “threshold allocation value” are presented to determine the players’ shares, and their properties, including efficiency, symmetry, and uniqueness, are proven. Finally, a numerical example shows that the proposed model can model threshold-based cooperation structures coherently and provides a suitable framework for future research in cooperative game theory.
Zinat Mehdidoust Jalali, Azamossadat Nourbakhsh, Sara Motamed,
Volume 17, Issue 2 (9-2026)
Abstract
Face recognition in real-world environments remains challenging due to domain shifts, limited labeled data, decision uncertainty, and constrained computational resources. This paper presents a novel neuro-symbolic framework that combines a SOAR-based cognitive meta-controller, cost-aware reinforcement learning, and domain adaptation to jointly improve recognition accuracy, computational efficiency, and inference latency. A face embedding network is first trained on the source domain and then adapted to the target domain using parameter-efficient fine-tuning (PEFT) and Domain-Adversarial Neural Networks (DANN). To enhance robustness under limited supervision and distribution shifts, the framework further incorporates few-shot learning and test-time adaptation (TTA). The proposed meta-controller is formulated as a Markov Decision Process (MDP), enabling dynamic allocation of computational resources based on image quality, uncertainty estimation, and recognition status. Symbolic knowledge encoded in the SOAR architecture guides the reinforcement learning policy through a neuro-symbolic gating mechanism, improving both interpretability and decision consistency. Experiments on the IJB-C and MegaFace benchmarks demonstrate significant improvements in face recognition and open-set recognition performance, while PEFT reduces trainable parameters by more than 95% without increasing inference latency. Overall, the proposed framework offers an accurate, adaptive, interpretable, and computationally efficient solution for real-world face recognition applications.
Seyyed Mehdi Hosseini, Mohammad Saidi-Mehrabad, Rouzbeh Ghousi, Ahmad Makoui, Mohammad Mahdi Paydar,
Volume 17, Issue 2 (9-2026)
Abstract
Industrial Tourism (IT) can lead to economic prosperity, job creation, capital attraction, and greater interaction between industry and society. On the other hand, the Industrial Tourism Supply Chain (ITSC) comprises activities and stakeholders, including transportation, industrial units, accommodation centers, and facilitators, whose coordinated performance directly affects the quality of tourists' experience and the efficiency of this industry. Therefore, the present study examines ITSC considering sustainability aspects to address gaps in the field. Moreover, the important issue of destination attractiveness is considered in the proposed model. The proposed model is solved using the Revised Multiple-Choice Goal Program (RMCGP) method. To evaluate the proposed model, a case study in Mazandaran province, Iran, is considered, and a sensitivity analysis is conducted on some key parameters. The output results show that the activity in this industry and the provision of services are economically justified.
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