Journal of Quality Engineering and Management

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: The objective of the present study is to determine an optimal configuration in terms of the type and number of components in order to maximize system availability and reduce costs, using an active redundancy allocation strategy, while considering load-sharing capability and the use of maintenance personnel under maintenance and leave policies, in the electrical power distribution system of a marine vessel. In the active redundancy strategy, all additional components and subsystems are operated simultaneously from the start of system operation, and the system fails only when all components have failed.
Methodology: In this study, a bi-objective model is developed for an electrical power distribution system with active redundancy in a marine vessel, where the first objective is minimization of total cost and the second objective is maximization of system availability. System behavior is simulated using a Markov chain and a phase-type distribution, and the model is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Failure of one component affects the failure rates of other components within the same subsystem, leading to an increase in their failure rates. In other words, the problem is analyzed under a load-sharing condition. A single repairman is considered for equipment repair. The maintenance and leave policy is defined such that if a component fails during the repairman’s leave period, the leave is terminated and repair of the failed component begins immediately. If another component fails while a component is under repair, it is placed in a repair queue, and the repairman starts repairing the next failed component immediately after completing the repair of the previous one. When the repairman is on leave and no component failure occurs, the repairman may resume the leave period.
Findings: The results of the study identify the optimal combination of the type and number of electrical power distribution panels in each subsystem of the vessel’s electrical power distribution system, aimed at increasing system availability and reducing costs through the use of active redundancy. In addition, the results provide the probability of the repairman being busy, which can support managerial decision-making regarding maintenance and leave policies.
Originality/Value: Considering the innovative aspects of the study, the results can be effectively used for engineering analyses, particularly in evaluating system availability, as well as for managerial analyses, including cost estimation and the allocation of maintenance personnel.

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: Effective management of organizational processes and facing challenges is crucial for organizations such as the Cooperative Investment Guarantee Fund to achieve their goals in today's competitive world. This issue is doubly important for this fund, given its wide range of stakeholders and its key role in supporting the cooperative sector. Therefore, the present study aimed to present challenges and solutions for improvement in the processes of issuing cooperative development guarantee credit insurance policies.
Methodology: This research is based on the principles of quality management and a process approach to ensure the scientific and practical validity and reliability of the results. In-depth analysis of the challenges and their prioritization was carried out using the Shannon entropy method, TOPSIS technique, and Nominal Group Approach (NGT).
Findings: The research findings showed that most of the fund's problems are concentrated in the process and strategy sections; therefore, in accordance with the extracted priorities, optimization solutions were presented and Key Performance Indicators (KPIs) were developed for continuous monitoring.
Originality/Value: In addition to creating process transparency, the final results of this research, by providing an operational and scientific roadmap, provided a basis for focusing resources on key points of success, which is a pivotal step towards reducing time and cost, improving effectiveness, and achieving strategic goals in the cooperative sector.