Modern Research in Decision Making

Abstract The rapid growth of online retail and the emergence of advanced technologies have necessitated a reassessment of quality maturity. Quality 4.0, as a novel paradigm in the Industry 4.0 era, emphasizes intelligent communication, automation, data analytics, and system integration. This study aims to provide a comprehensive framework for evaluating Quality 4.0 maturity in the Iranian online retail sector. A mixed-methods approach was adopted and carried out in four phases: first, a systematic literature review identified 18 initial dimensions of Quality 4.0; second, the conceptual model was validated through a survey of 194 e-commerce experts and structural equation modeling, resulting in the confirmation of 10 core dimensions; third, a five-level maturity model (ranging from initial to leading) was designed based on literature and expert opinions; and fourth, a fuzzy inference system was developed to assess and measure maturity levels. For validation, the framework was applied as a case study in Kourosh E-commerce Company (Okala). Findings indicated that data, analytics, culture, leadership, and system integration were the most influential dimensions. The proposed framework provides a practical tool for managers and decision-makers to guide the adoption and implementation of Quality 4.0 in online retail.

Abstract The urgent need to curb emissions has prompted governments worldwide to support green technologies, including carbon capture and utilization (CCUS). This study focuses on selecting an optimal portfolio of CCUS technologies for government incentives in Iran. Given the uncertainties in technological advancement, the proposed model integrates interactive optimization with dominance-based rough set theory to enhance transparency and objectivity in decision-making. Using data from the Power Research Institute of Iran, the model follows three steps: initial selection, extraction of decision-making rules, and iterative optimization. This approach identifies the best CCUS portfolio while ensuring a systematic and transparent decision-making process. The model refines decision-making rules by combining interactive optimization with the analytical power of rough set theory, making policy implementation more effective. The results show that this framework can streamline environmental policymaking, offering a structured way to allocate incentives. Its adaptability makes it useful for other domains beyond climate initiatives, ensuring governments can make well-informed, strategic decisions with clarity and precision.

Abstract Discrepancies in judicial rulings stemming from judges' broad discretionary powers in applying subjective sentence mitigation pose a fundamental challenge to the criminal justice system. This issue not only leads to inconsistencies in judicial decisions but also undermines public trust in the judicial system. The present study aims to reduce this heterogeneity and provide a systematic solution by designing an intelligent decision support system for judges, combining fuzzy inference and the Fuzzy DEMATEL method within a four-level hierarchical structure.
In the first step, using the Fuzzy DEMATEL method, the significance of each mitigating condition outlined in Article 38 of the Islamic Penal Code was determined based on input from six expert judges. This phase enabled precise prioritization of criteria influencing sentence mitigation. Subsequently, a hierarchical fuzzy system with an 83-rule knowledge base was designed, aligned with expert knowledge and legal principles. The implementation of this model on a real criminal case demonstrated its effectiveness in producing consistent and well-reasoned judicial opinions.
The primary innovation of this research lies in the novel integration of fuzzy and hierarchical methods within the realm of criminal law. This model not only provides an objective standard for applying sentence mitigation but also serves as a template for developing intelligent systems in other legal domains, such as determining restitution, mitigating financial crimes, or assessing aggravating circumstances. The findings of this study can significantly contribute to enhancing consistency in judicial rulings, reducing human errors, and increasing transparency in the adjudication process.

Abstract Risk assessment in the New Product Development (NPD) process within the detergent industry is an inevitable necessity due to rapid changes in customer preferences, intense market competition, and continuous technological advancements. Effective management of these risks and the implementation of appropriate preventive strategies not only increase the likelihood of product success but also contribute significantly to improving quality, reducing time-to-market, and enhancing customer satisfaction.
This study employs an integrated approach based on the SWARA and QFD methods under a Fermatean fuzzy environment to identify NPD-related risks and evaluate risk mitigation strategies. Eight risk factors and eight corresponding preventive strategies were identified through a comprehensive review of the literature and validation by industry experts in the detergent sector. The SWARA method was applied to determine the relative importance weights of the risk factors, while the QFD method was used to analyze the relationships between the identified risks and their corresponding mitigation strategies.
The application of this hybrid approach in a Fermatean fuzzy environment provides greater flexibility and comprehensiveness in representing uncertainty and ambiguity in decision-making, thanks to the broader computational scope of Fermatean fuzzy sets.
The results indicate that technical, market, and supply chain risks are the most significant factors affecting the NPD process in the detergent industry. Furthermore, the strategies of "optimizing production processes ", "market research and competitor analysis ", and "product testing and validation " have been prioritized as the most effective preventive actions.

Abstract Simultaneously managing product assortment, pricing, and substitution effects in omni-channel retailing is a complex challenge due to interactions among customer demand, operational constraints, and price competition. Most previous models focus on only one of these areas, overlooking their interdependencies.
This study proposes an integrated nonlinear model that maximizes profit by considering stochastic demand, multi-stage substitution (within and across channels), inventory constraints, shelf space limitations, and fulfillment center capacities.
To solve the model, four metaheuristic algorithms—Forest Optimization, Particle Swarm Optimization, Imperialist Competitive Algorithm, and Genetic Algorithm—are employed. Their performance is evaluated using real-world data from the largest retailer in Iran and synthetic datasets. The results show that Forest Optimization significantly outperforms the other methods, achieving an average 20% increase in profit and a 15% reduction in lost sales. Additionally, it converges 25% faster, and the stability of its results is confirmed by Wilcoxon tests at a 0.05 significance level.
Scenario analysis reveals that increasing the number of channels can boost profitability by up to 30% and reduce internal competition, while expanding product variety can lower lost sales by up to 18%. These findings provide practical guidance for retailers to optimize pricing, inventory, and assortment decisions across both physical and online channels.

Abstract Students, as future professionals and key players in social networks and community development, face various psychological challenges. Early detection and timely intervention can prevent the emergence of more serious issues. Therefore, aiming to provide an innovative framework for predicting students’ mental health, this study proposes an intelligent integration of multi-criteria decision-making (DEMATEL1), improved principal component analysis2, and machine learning techniques. First, based on a comprehensive dataset of student information and using DEMATEL technique, the complex structure of relationships between factors affecting students’ mental health is identified. Then, the target variable (mental health status) was extracted through improved principal component analysis. In the modeling phase, accordingly to comparing five machine learning algorithms, the logistic regression model was selected as the superior model due to its 100% accuracy and high interpretability. Combining the probabilistic output of this model with other algorithms improved all models’ accuracy to 100%. Coefficient analysis revealed that psychological variables-such as academic anxiety, suicidal thoughts, and depression-had the most negative impact, while marital status and interest in the field of study had protective effects on students’ mental health. The results of this study can be used as a powerful tool for early identification of students who need psychological support.