Journal of Quality Engineering and Management

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.

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: 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: This study aims to develop a mathematical model for flexible job shop scheduling. The primary focus is on optimizing three objectives: the makespan, the maximum machine workload, and the total workload. The ultimate goal is to enhance productivity and flexibility in manufacturing systems.
Methodology: Two metaheuristic algorithms, NSGA-II and MOGWO, were used to solve the model. The model was first validated on a small scale, and then a sensitivity analysis was conducted on larger instances. The performance of the algorithms was compared based on accuracy and solution quality metrics.
Findings: The results indicate that MOGWO performs better on medium-sized problems, whereas in large-scale cases, the difference between the two algorithms is not significant. The highest sensitivity was observed among the objectives regarding production and maintenance costs. Additionally, a resource-allocation pattern and an optimal sequence of operations were derived.
Originality/Value: The originality of this research lies in developing and applying a multi-objective mathematical model for flexible job-shop scheduling that considers real-world constraints, including costs and resource limitations. The simultaneous use and detailed comparison of NSGA-II and MOGWO across different problem sizes is another contribution. Furthermore, the proposed operational pattern improves the applicability of the results in industrial environments.

Abstract Purpose: In recent years, environmental management, with a focus on environmental protection, has become one of the main priorities of governments and organizations. One key area in this regard is the design of supply chain networks with environmental approaches, which helps integrate production processes with sustainability goals.
Methodology: This study aims to investigate the feasibility of using palm tree pruning waste in the production of Medium-Density Fiberboard (MDF). As one of the richest plant resources in the country, the palm tree has high potential for application in the wood industry and for reducing environmental waste.
Findings: First, the biological and structural characteristics of palm trees are examined. Then, using this raw material, a supply chain network design model for MDF production is developed. The model's performance is evaluated and compared under different operational and environmental conditions.
Originality/Value: The findings indicate that using palm waste in MDF production not only helps reduce environmental waste but also contributes to the design of an efficient and green supply chain. Moreover, the study highlights a lack of prior research on this specific topic, underscoring the innovative aspect of the work.

Abstract Purpose: Calculating the optimal target mean for a process is recognized as an essential research area, with many proposed models in the literature. Previous studies have typically focused on a single market. The main difference in this research lies in the number of markets considered; unlike previous works, this study examines n different markets simultaneously.
Methodology: This study aims to determine the optimal process quality mean for a limited number of markets based on the target values of quality characteristics in each market. We propose a model to calculate this optimal mean across n markets with different price/cost structures. A key innovation of this research is the incorporation of probability distributions that reflect the likelihood of the quality characteristic falling within specific quality ranges in each market.
Findings: The model considers the probability that the quality characteristic falls within each market's defined quality range. To analyze and solve the model, absorbing Markov chains are used. A numerical example is presented in which the model is applied to two markets, and the optimal target mean and corresponding optimal revenue are obtained.
Originality/Value: Based on the results from the numerical example, the optimal target mean and revenue were determined for the two markets. A sensitivity analysis was conducted to assess the influence of various model parameters on these parameters, demonstrating how changes in parameters impact the model's outcomes.

Abstract Quadcopters are a special type of unmanned drones that have many applications in today's world. Due to limited resources, the design of a system must be done in such a way as to achieve the highest possible amount of reliability based on our limited resources.
For this purpose, first the reliability of each subsystem was calculated. Then the reliability was optimized using computer algorithms. One of the conventional methods of increasing the reliability of systems is to use redundancies, but due to its limitations Finance and mass for quadcopters, we cannot use any number of extras to increase reliability. Therefore, optimization should be used. The most famous meta-heuristic algorithms can be mentioned as Genetic Algorithm, Coco, Ant Colony, Gray Wolf, etc.
With the help of the firefly algorithm and reliability model, the quadcopter was checked in the presence of redundancy in terms of cost and mass minimization and having the most optimal reliability, and the resulting results were validated by genetic algorithm.

Abstract Abstract: One of the basic issues in Ultrahigh-dimensional data analysis is fitting the optimal model and estimating its unknown quality parameters in such a way that it can correctly interpret the structure of the investigated data. In this article, we compare two non-local hyper priors: hyper product moment and hyper product inverse moment priors in determining the optimal model at the same time as estimating the parameters in variable selection using Bayesian Shrinkage in ultrahigh-dimensional generalized linear models. In order to compute the posterior probabilities, the Laplace approximation method was used, and to select the optimal model in the model space of posterior probabilities, Simplified shotgun stochastic search algorithm with screening (S5) for GLMs was used along with screening. Finally, through the study of simulation and real data analysis, the effectiveness of the above Bayesian Shrinkage methods has been evaluated with the ISIS-LASSO and ISIS-SCAD method. The advantage of the model is shown.

Abstract Reliability is one of the most important issues in the engineering design process. When we use a system, we often want to determine the reliability of this system. Clearly, higher reliability systems are more valuable. On the other hand, the reliability of each system depends on the structure and reliability of its components. Therefore, to increase the reliability of the system, the reliability of its components can be improved. In addition, to increase the reliability of the components of a system, it is necessary to consider its costs. This study, by considering a series-parallel system, determines the amount of increase required for the reliability of system components so that the reliability of the whole system is maximized and the cost of this increase does not exceed a predetermined value. The following is a numerical example for reviewing the results. Finally, a summary of the results of the article is given.

Abstract Nowadays, competition in various industries focuses on reducing production waste and improving the quality of manufactured products. In this article, the improvement of surface roughness quality of AISI 4340 workpieces in the turning process using TiN-coated carbide tools is investigated. For this purpose, the parameters affecting the surface quality, namely cutting speed, feed rate, and depth of cut, were selected as the input factors for the experiments. Then, the degrees of freedom of the system and the required number of levels were determined, and the corresponding orthogonal arrays were calculated. Accordingly, twenty-seven experiments were designed to measure the surface roughness values, and for each experiment, three measurements of the surface roughness parameter were recorded. Next, by calculating the grey ratios, grey coefficients, and grey grades using the relevant formulas, the final grey relational graphs were plotted for all three levels of the experiments. Based on the grey relational diagram, the percentage influence of each input parameter on achieving the desired surface roughness was determined, and the optimal numerical values for these parameters were identified. A comparison of the grey analysis results with the actual experimental data confirms the accuracy and capability of this method in predicting surface roughness in the turning process.