Journal of Quality Engineering and Management

Abstract Given the dynamics of the competitive market and changing customer needs, quality plays a key role in meeting customer requirements, and delivering high-quality products is essential for the survival and growth of manufacturing organizations. In this paper, process planning and advanced scheduling are considered, where scheduling is performed based on the orders received by the system. The production system has flexible machines and operators, and orders must be scheduled according to the current status of the machines and available operators so that, by assigning appropriate machines and operators, quality-related costs are minimized. To achieve the desired quality, the Taguchi quality loss function is developed by linking the quality characteristics of operations to the assigned operator and machine. Since the orders have precedence-required operations and, in one aspect, are similar to multiple traveling salesman problems, providing an exact mathematical model for these problems is often complex. Moreover, when dealing with flexible machines and operators, this complexity increases. In most works related to integrated process planning and scheduling, the mathematical models presented (because they define, for operations, a set of binary ordered pairs indicating precedence or non-precedence between operations) lack efficiency for solving with optimization software. Therefore, in this study, the problem is modeled with a new approach, and the problem is solved using GAMS software. For larger-scale problems, given the existing complexities, a genetic algorithm is used.

Abstract Workforce health is one of the most important fundamental prerequisites for productivity and plays a crucial role in achieving sustainable socio-economic development. Therefore, preventing accidents in work environments, as a public necessity, has always attracted the attention of specialists. Given the key role of unsafe behaviors in the occurrence of accidents, some researchers have focused on monitoring unsafe behaviors using statistical process control (SPC) charts. However, human behavior is influenced by various factors such as environmental conditions and individual characteristics. These factors increase process variance and are often referred to as common causes of variation. In such processes, identifying the most influential factors contributing to variance can significantly improve process performance. Nevertheless, such sources of variability have not been adequately considered in previous related studies. The main objective of this paper is to model and monitor different levels contributing to variance in individuals’ unsafe behaviors. The proposed model was implemented in a manufacturing company. After determining the variance associated with each factor, the variability of each component was analyzed separately. The proposed approach, which demonstrates a high capability in reducing unsafe behaviors, can be used as an effective tool for safety management in organizations.

Abstract In today’s competitive markets, quality is a fundamental pillar for organizational survival. One important dimension of quality is reliability; therefore, improving reliability is considered essential. To enhance system reliability, approaches such as adding redundant and standby components can be employed, and an optimal design can be developed using available system data. In some cases, system data lack sufficient accuracy, and fuzzy logic is used to enable more precise analysis. In many practical applications, in addition to improving system reliability, other objectives such as minimizing system weight, volume, or cost are also considered in optimal design. Since these objectives are conflicting, the system design problem becomes a multi-objective optimization problem. In this paper, a mathematical model is proposed with the objectives of maximizing reliability and minimizing cost for multi-stage series–parallel, standby, and k-out-of-n systems, subject to system volume and weight constraints. The decision variables in this problem are the optimal number of redundant components allocated to each stage. Finally, a numerical example is presented in which system component reliability over the life cycle is considered, and the cost, volume, weight, and failure rate parameters of components are modeled as triangular and trapezoidal fuzzy numbers. The resulting model is solved using the metaheuristic simulated annealing algorithm, and a set of Pareto-efficient solutions is obtained. The results indicate that standby systems achieve higher reliability compared to the other system configurations.

Abstract Six Sigma methodology has been established as a process-oriented approach with strong emphasis on results and effectiveness, aiming to improve the quality of products, services, and processes. In our country, in line with global developments, industrial and service organizations have rapidly become aware of the high potential of this methodology through information exchange with international organizations and industrial centers, and have been encouraged to implement it. The main objective of this methodology is the implementation of a measurement-based strategy focused on process improvement and variability reduction. Sarcheshmeh Copper Complex, as one of the major and foundational industries of the country, has defined numerous Six Sigma projects to improve production processes, reduce waste, and increase productivity, achieving extensive benefits such as improvement of production processes, reduction of losses and scrap, and significant financial savings. Since one of the main concerns of managers at the Sarcheshmeh Copper Complex is reducing the amount of cold material produced in the smelting plant and consequently preventing the waste of a considerable portion of incurred costs a Six Sigma project was therefore considered to address this issue. In this research, simulation tools were also utilized to develop an integrated model for reducing cold material in the smelting process. For this purpose, a discrete-event system simulation approach was employed. The design and implementation of the simulation model were carried out using Arena software and Visual Basic.

Abstract The success of service organizations can be attributed to customer orientation and attention to service quality. Due to the importance of service quality in service industries and its significant impact on customer satisfaction, the question of how service quality can be measured has always been raised. Attention to quality in all organizations seeking excellence in the service sector is considered one of the most important concerns of managers. Moreover, creating quality in an organization requires attention to a set of influential dimensions that must be considered in service delivery. By reviewing the research literature, the dimensions and components for measuring service quality in the banking industry were identified. After refining the dimensions, they were categorized and then incorporated into a questionnaire distributed among bank employees and customers. The results of this study indicate that among the identified dimensions, courtesy and politeness received the highest scores from both customers and bank employees, reflecting its importance from the perspectives of both groups. Among the quality dimensions, tangibles received the lowest weight in both respondent groups compared to the other dimensions, indicating their relatively lower importance from the viewpoints of customers and employees.