Journal of Quality Engineering and Management

Abstract Purpose: The vaccine cold supply chain, as a quality-sensitive service–operational system, plays a critical role in ensuring timely delivery, maintaining vaccine efficacy, and minimizing wastage. The occurrence of operational and disruption risks intensifies process variability, undermines system reliability, and degrades service quality. The objective of this study is to develop a quality-oriented framework for the modeling and optimization of the COVID-19 vaccine cold supply chain, with a particular emphasis on quality-related performance indicators under conditions of uncertainty.

Methodology: In this study, a novel multi-period and multi-product simulation–optimization framework is developed to support decision-making in vaccine inventory management, allocation, and distribution under operational and disruption risks. The proposed approach integrates agent-based simulation with optimization techniques. The simulations are configured based on scenarios involving transportation disruptions and vaccine supply disruptions and are benchmarked against a disruption-free case.

Findings: The results are evaluated using several key performance indicators, including the expected vaccine delivery time, service level, vaccine wastage due to vehicle failures, and financial metrics. The simulation results indicate that the disruption-free scenario achieves the highest service level (0.82) and greatest degree of performance robustness, whereas transportation disruptions result in the spoilage of 8.9 million vaccine doses, and vaccine supply disruptions lead to the lowest service level (0.76). Statistical validation using the paired sign test further confirms the significance of these differences at the 95% confidence level.

Originality/Value: The present study adopts a quality engineering perspective to analyze the vaccine supply chain as a service system sensitive to process variability and proposes a quality-driven risk management framework. The findings provide practical insights for policymakers to enhance system reliability, reduce quality-related costs, and improve the resilience of vaccine distribution systems under crisis conditions.

Abstract Purpose: This study aims to develop a durable closed-loop supply chain network capable of simultaneously addressing sustainability, resilience, agility, and digitalization while incorporating fuzzy–stochastic uncertainties. The significance of this research lies in the limitations of traditional supply chains, which often fail to perform effectively under severe environmental fluctuations, operational disruptions, and demand variability, thereby highlighting the need for intelligent and multidimensional decision-making frameworks.

Methodology: To achieve the research objectives, a structured three-phase framework was designed. In the first phase, demand—subject to considerable uncertainty—was forecast using the SARIMA time-series model to capture market volatility and seasonal patterns. In the second phase, supplier evaluation criteria were identified through a systematic literature review and expert judgment, and subsequently weighted via the stochastic–fuzzy Best–Worst Method (SFBWM). Supplier ranking was then performed using the stochastic–fuzzy TOPSIS (SFTOPSIS) technique. In the final phase, a multi-objective fuzzy–stochastic mathematical model was developed to design and optimize the supply chain network, while fuzzy–stochastic robust optimization was employed to address data uncertainty. The multi-objective problem was solved using a modified version of the lexicographic–Chebyshev multi-choice goal programming method (LCRMCGP).

Findings: A case study conducted in “Ebtakar Tajhiz Teb Yekta,” a company operating in the medical equipment industry, demonstrated that the proposed model effectively supports key strategic decisions, including the selection of primary and backup suppliers, the optimal location of collection and recycling centers, excess capacity allocation, and the choice of information-exchange technologies (traditional systems vs. blockchain-based platforms). The integration of IoT and blockchain technologies increased product return rates, reduced recycling costs, and enhanced transparency and sustainability across the network. Overall, the results confirm that the proposed framework can successfully balance economic, environmental, and social objectives while improving flexibility and resilience under uncertainty.

Originality/Value: The novelty of the present study lies in developing an integrated framework for designing a viable closed-loop supply chain under hybrid fuzzy–stochastic uncertainty. Unlike previous studies that mainly focused on isolated dimensions of supply chain management, this research simultaneously incorporates sustainability, resilience, agility, and digitalization within a multi-objective optimization model. Furthermore, the integration of SARIMA, SFBWM, SFTOPSIS, and LCRMCGP methods provides a more accurate and comprehensive decision-making process. Comparative results also demonstrate that the proposed model outperforms conventional approaches in reducing deviations, improving decision consistency, and enhancing overall network sustainability.

Abstract Purpose: The study aims to develop a new Bayesian estimation approach, termed the E2-Bayesian method, for estimating the traffic intensity parameter in the multi-server M/M/c/∞ queueing system. Given the crucial role of accurate efficiency estimation in optimizing service systems, this research addresses the need for more reliable inference under uncertainty.
Methodology: The M/M/c/∞ queueing model, characterized by servers, exponential interarrival times with rate parameter λ, and exponential service times with rate parameter μ, is considered. The traffic intensity parameter is estimated using Bayesian, E-Bayesian, and the newly proposed E2-Bayesian methods under the general entropy loss function. The performance of the proposed estimator is assessed through Monte Carlo simulation and validated using a real dataset.
Findings: Simulation results and empirical analysis demonstrate that the proposed E2-Bayesian estimator outperforms the traditional Bayesian and E-Bayesian estimators in terms of efficiency and accuracy. The estimator that minimizes the mean waiting time of customers in the queue is identified as the optimal choice.
Originality/Value: This research introduces a novel E2-Bayesian estimation approach that enhances the precision of parameter estimation in queueing models under uncertainty. The integration of the general entropy loss function provides a flexible and robust framework, contributing to the advancement of Bayesian inference in stochastic systems.

Abstract Purpose: This research aims to empirically analyze the low beta anomaly within the framework of random basket theory using linear and quantile regression. This financial anomaly refers to the higher long-term returns of a portfolio of low-beta stocks than of a portfolio of high-beta stocks. This study examines the excess growth rate generated in a random portfolio based on this financial anomaly. The statistical population of this study consists of 8 stocks from the US stock market during the period 2015 to 2023.
Methodology: To achieve the research objectives, a continuous-time dynamic model with analytical solutions is proposed. To find its optimal weights or strategies, the "functionally generated portfolios" approach and the concept of "generating functions" are used. Finally, a regression analysis of US stock market data is conducted to examine the growth rate generated in this model.
Findings: The results show that investors are always trying to increase their investment returns by adopting an appropriate method. In this regard, higher returns from low-beta investment portfolios have been observed over the past few decades, and the use of random portfolios as a reasonable method to examine the portfolio's excess return in this financial anomaly is thus crucial.
Originality/Value: Given the innovative nature of this research in using stochastic portfolio theory to examine the excess growth rate generated based on the low beta anomaly, the results can help investors construct optimal portfolios with higher long-term returns.

Abstract Purpose: The existing supervisory system in many countries, especially in Iran, mainly uses general and integrated models in which the structural, mission, and operational differences between banks and financial institutions have not been properly taken into account. This uniform approach has led to reduced risk identification accuracy, a lack of adaptation to each financial institution's specific needs, and reduced effectiveness of supervisory measures. The main objective of the present study is to design a cause-and-effect model to improve the quality of supervision based on the mission type of banks and financial institutions, using a mixed approach (Meta-Synthesis-Fuzzy DEMATEL).
Methodology: The present research was conducted using a mixed method (Qualitative-quantitative) and exploratory approach. Then, using a survey, 25 banking industry experts with at least 10 years of executive experience in finance and banking and master's and doctoral degrees were recruited to examine the validity and reliability of the proposed model. Also, paired-comparison questionnaires were distributed to the experts, and the intensity of impact and effectiveness between the research dimensions were examined using the fuzzy multi-criteria decision-making technique, DEMATEL.
Findings: The research findings show that using the meta-synthesis approach, 9 dimensions and 40 components were selected. They were selected from the dimensions. Also, the results of fuzzy DEMATL analysis show that the most influential dimension of the present study regarding the supervision of banks and credit institutions based on the value of (D+R) is the legal and regulatory supervision dimension among the independent dimensions and the cause with the highest value and the most influential variable. Also, among the dependent and affected dimensions based on the lowest value (D-R), the environmental and social performance dimension was recognized as the most influential variable in improving the quality of supervision of banks and credit institutions.
Originality/Value: Using a meta-combination approach in the analysis of previous research, which can provide new horizons for designing effective models based on improving the quality of banking system supervision. Therefore, the present study is of high scientific and practical importance for advancing methodology, responding to the current needs of the country's financial system, and improving the effectiveness of supervision of monetary institutions. This research has also led to recognition of the intensity of the relationships among the dimensions of quality improvement in banking industry supervision and has drawn more attention to these components.

Abstract Purpose: In this paper, a multi-objective mathematical programming model is presented for designing a multi-level, multi-period fruit supply chain, while accounting for sustainable development goals encompassing cost minimization, greenhouse gas emission minimization, and social dimension maximization. In the proposed model, product quality plays a fundamental role in supply chain design, and fruits are graded in distribution centers based on quality and distributed to fruit markets, compost factories, juice factories, concentrate factories, and drug factories.
Methodology: The proposed multi-objective model is solved using fuzzy goal programming. The weights of the objective functions as well as the weights of social dimensions are calculated using the fuzzy best-worst approach.
Findings: In this research, a case study of Fars province, the third-largest apple-producing province in the country, is used to evaluate the performance of the proposed model. The results of the proposed model were compared with three models from economic, environmental, and social perspectives. The research findings show that system sustainability cannot be achieved by separately optimizing models with economic, environmental, and social perspectives. The proposed model achieves an efficient optimal solution across all three dimensions of sustainability, with significant improvements in the environmental and social dimensions and a negligible increase in system costs. In addition, by establishing a proper balance across all three sustainability dimensions, the proposed model leads to a supply chain with a different network structure and a different number of deployed facilities compared to the other three models.
Originality/Value: The added value of this research is to provide a comprehensive model that considers sustainability and quality as the main factors in grading and distributing products across different levels of the supply chain. The findings of this research can help policymakers and operational managers in the fruit industry make strategic and operational decisions in fruit production, processing, and supply-to-sale markets based on sustainability dimensions.

Abstract Purpose: This research aims to present a model for creating shared value in startups based on new technologies.
Methodology: To achieve this goal, a mixed-methods approach was employed, consisting of two phases: qualitative and quantitative. In the first phase, a review of the theoretical and empirical foundations of the topic was conducted, and to enrich the results, insights from a selected group of experts were utilized. This group consisted of 9 experts specializing in new technologies and startups, selected through purposive sampling based on theoretical saturation.
Findings: Based on the results, 17 main categories, 161 sub-components, and 1346 concepts were identified in this research. In the quantitative section, the model's dimensions and components were prioritized using pairwise comparison questionnaires and fuzzy hierarchical analysis.
Originality/Value: According to the findings, customers and services are the central focus of activities, and startups, by deeply understanding customer needs and desires, provide products and services aligned with their values. Collaboration and networking with customers and business partners facilitate knowledge exchange and innovation, which is a fundamental element that enables startups to address customer problems using new technologies. Additionally, attention to product and service quality, attracting and retaining top talent, and securing financial resources are other important aspects of this model that lead to increased customer satisfaction and trust.

Abstract Purpose: Given the increasing complexity and uncertainty in today's organizational environments, making informed, timely, and flexible decisions is of paramount importance for ensuring the sustainability and long-term growth of organizations. Accordingly, the present study aims to identify and prioritize individual, group, organizational, technological, and environmental factors that influence the quality of managerial decision-making, thereby providing a practical framework for enhancing decision-making capacity and increasing organizational resilience.
Methodology: This study is applied in nature and adopts a descriptive-analytical approach. Initially, a comprehensive set of factors was extracted through a systematic literature review. Then, a five-member expert panel was formed to identify the evaluation criteria, and the required data were collected using intuitionistic fuzzy numbers. The relative weights of the criteria were calculated using the FUCOM method, and the alternatives were finally prioritized using an aggregated decision matrix based on the multi-criteria WASPAS method.
Findings: Specialized knowledge is the most significant factor influencing the quality of managerial decision-making. It was followed by participative decision-making and data accuracy and reliability, which ranked second and third, respectively. In contrast, factors such as organizational structure and the legal, cultural, and social environment had the least impact. Overall, these results highlight the importance of focusing on internal and controllable factors to enhance organizational sustainability in dynamic environments.
Originality/Value: This research is among the first to provide a comprehensive prioritization of individual-to-environmental factors affecting the quality of managers' decisions by integrating a systematic literature review, intuitionistic fuzzy numbers, the FUCOM method, and the WASPAS method. The resulting framework not only enriches theoretical understanding but also serves as a practical guide for policymakers and managers in optimal resource allocation.

Abstract Purpose: The purpose of this paper is to address one of the most critical challenges faced by organizations today: controlling carbon dioxide emissions. This study aims to provide a model for designing a green supply chain network that minimizes total network costs while incorporating environmental considerations. The research seeks to achieve a balanced optimization of costs, carbon emissions, and service levels in supply chain management.
Methodology: This study proposes a novel integrated optimization model that considers economic, environmental, and customer satisfaction aspects within the supply chain network. The mathematical model is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem. An exact method is employed to solve the model, which is coded and implemented using GAMS optimization software. The efficiency and effectiveness of the model are validated through numerical examples and data analysis.
Findings: The results demonstrate the model's ability to optimize both economic and environmental dimensions while maintaining high service levels and customer satisfaction. The numerical examples, solved for problems of varying dimensions, confirm the practicality and effectiveness of the proposed approach. The findings highlight the trade-offs between cost minimization, carbon emission reduction, and service quality in supply chain networks.
Originality/Value: This research contributes to the field by presenting a new integrated optimization model that simultaneously addresses cost efficiency, environmental sustainability, and customer satisfaction in green supply chain design. The use of a mixed-integer nonlinear programming approach and its implementation in GAMS provides a robust framework for solving complex supply chain problems. The study offers valuable insights for organizations aiming to achieve sustainability goals while maintaining economic viability and customer-centric operations.

Abstract Purpose: Given that determining the aggregate coefficient is a key constraint in Aggregate Production Planning (APP), this study seeks to identify the factors influencing this coefficient and to analyze them using Fuzzy Interpretive Structural Modeling (FISM) to explore their interrelationships.
Methodology: After identifying the key factors influencing the aggregate coefficient, an Interpretive Structural Modeling (ISM) questionnaire was distributed among experts, and the responses were aggregated. Subsequently, the FISM steps were carried out. Finally, an interaction network was constructed, and an analysis was performed to evaluate the degree of dependence and driving power among the identified factors.
Findings: The developed interpretive structural model comprised 11 hierarchical levels. The fuzzy analysis of dependence and driving power indicated that none of the factors were categorized as autonomous, reflecting a strong degree of interconnection among the variables within the model.
Originality/Value: Production planning for multiple products utilizing shared resources is a complex challenge. Thus, analyzing the variables that influence the determination of the aggregate coefficient in production planning provides valuable insights, facilitating informed decision-making, particularly in optimizing resource allocation to achieve an optimal production level.