Journal of Industrial Engineering and Management Studies

Abstract Operations management in service organizations has become a significant focus for researchers and decision-makers in recent years. Accordingly, scheduling problems, which are the process of allocating resources within a specific planning horizon, are fundamental to every service system. Corporations need to satisfy some recurring service requirements in such systems where the efficient allocation of resources and effective time management are vital for improving operational processes. This problem, known as multi-period service scheduling, includes customers with periodic demands for specific services. By investigating the related study, no research has been found that surveyed the different visit patterns of customers. This is the first study to provide a mathematical model considering customers' preferences concerning various visit patterns. Despite its complicated structure, the problem is formulated as a new Pure Integer Linear Programming (PILP), minimizing the total number of operators required during the planning horizon. This study uses a numerical example and a real case study to confirm the validity of the proposed model. The practical implications of this research are significant, as it presents a model that can effectively solve real-world, large-scale problems with reasonable computing time and full compliance with all constraints, thereby improving operational efficiency and customer satisfaction.

Abstract Using information systems (IS) infrequently and ineffectively after initial adoption may result in unnecessary costs or waste of effort. Most continuous models for information systems are concerned with how users accept and use IS. In the original marketing research and modeling of consumer repurchase behavior, these models were developed to assess loyalty and continuity within individual levels of system usage or in the individual acceptance of systems. They were developed for marketing research and to model repurchase behavior. To study the continuation of systems, effective factors of continuation for organizational users are not taken into account. To identify factors that influence the continuation of information systems, we reviewed the literature and conducted semi structured interviews to identify the main factors. Independent variables include governors, managers, leadership styles, technology change, strategies, users, customer expectations, culture, law, and transparency, Using Atlas T/I software in the last step, the final model of research is customized in the system of communication and information technology by adding mediator variables like switching costs and positive and negative switching costs.

Abstract During the past decades, various models were presented to estimate project duration but, we usually face with this problem that why the estimated values for finish time of the activities and the project don’t match with the actual values and there is always a huge gap between the estimated finish time of the project and the actual finish time of the project. The oldest method that has been widely used in the past decades is the Earned Value Management (EVM) methodology, which estimates the completion time of the project based on cost indicators. During the past years, researchers have developed new models such as Earned Duration Management (EDM), Earned Schedule Management (ESM) that each of them has tried to reduce the prediction error by focusing on the indicators presented in their proposed models. In this research, the Earned Resource Management (ERM) model has been developed, which present indicators for measuring the performance of activities based on the resources provided for the implementation of project activities, as well as the progress of project activities using these resources. It is a more suitable basis for evaluating how activities are implemented and also estimating their finish time. The proposed model is implemented on a construction project and the results show that the Mean absolute percentage error (MAPE) is about 3.12%, at the end of the project which is lower than other presented methods.

Abstract In recent years, the use of lean construction has been expanded to reduce waste and increase productivity in construction projects. Construction managers and engineers try to use innovative methods to reduce waste in construction projects. There are repetitive processes in construction in which waste can be avoided by reducing lag time. This research aims to optimize construction processes using the Value Stream Mapping (VSM) method. For this purpose, the process of Industrial Concrete Flooring (ICF) was selected as one of the sub-constructions of industrial buildings. First, the ICF process was mapped using field observations and recording the time of each activity. Second, the process was modeled using Discrete Event Simulation (DES). The DES model was improved after identifying the process’s bottleneck through the analysis of the developed model. The analysis showed that concrete leveling activity acts as a process bottleneck leading to an increase in the duration of the concrete remaining (about 51 min) in the truck mixer and a reduction in the productivity of workers and machinery. Finally, the developed model was modified. As a result, the duration of concrete remaining in the mixer was reduced to 18 minutes, and the utilization of labor and machinery was also improved.

Abstract Sudden and severe stock price crashes pose a significant challenge to stock markets. The substantial losses incurred from such events underscore the need for more effective forecasting tools. This study aims to enhance the predictive power of models for stock price crashes in Tehran Stock Exchange and commenced with a comprehensive literature review to identify key financial factors influencing stock price volatility. Given the high dimensionality of the dataset and the extended time period, metaheuristic algorithms were employed for feature selection. 10 algorithms, namely Ant Colony Optimization, Hill Climbing, Las Vegas, Whale Optimization, Simulated Annealing, Genetic Algorithm, Tabu Search, Particle Swarm Optimization (PSO), Honey Bee (HBA) and Firefly were utilized to reduce dimensionality and enhance model performance. Subsequently, Support Vector Machines were implemented to develop predictive models. The models were trained and evaluated using historical data from Tehran Stock Exchange spanning from 2001 to 2020. The findings of this research demonstrate that combining metaheuristic algorithms for model reduction and optimization, along with advanced machine learning techniques, yields results that can significantly improve investment decision-making.

Abstract This paper examines the joint optimization of production and maintenance planning for a single-machine deteriorating system. To achieve optimal performance and meet customer demand at the lowest cost, manufacturing companies need to carefully plan production and maintenance, considering various factors such as time, cost, output levels in any period and its impact on machine deterioration. In this research, we attempt to plan the production and maintenance process for a single-machine single-product system over multi-period. The machine has two operational states during production and gradually deteriorates as it ages. Maintenance operations restore the machine to healthy state and reduce the probability of producing defective products. We model the problem using the Markov decision process and employ the value iteration algorithm to determine the optimal policy, i.e., the best actions to take at each decision epoch. We evaluate the model's effectiveness by solving a numerical example and analyzing how changes in different parameters affect the results. The findings reveal the relationship between various parameters and the average cost rate. Changes in the mentioned rate due to changes in setup cost and the probability of producing conforming products are almost uniform without any drastic fluctuations. If the production cost of each item exceeds a certain threshold, the company's obligations are not enforceable.

Abstract The limitation of government resources, especially in the field of budget, for the development of infrastructure on the one hand and the possibility of defining multiple plans and projects on the other hand, makes the issue of prioritizing and choosing the best portfolio of feasible projects vital. The basic question is which projects should be done and how the projects will be managed. The model used to select the project portfolio should be realistic, capable, flexible, affordable, and simple. It is natural that the correct choice of the prioritization model and the selection of projects with the help of economic and non-economic criteria can help in the development of infrastructures as quickly as possible and in achieving the goals. In this research, we are trying to prioritize projects using the TOPSIS method and the innovative method of fuzzy group decision making. Among the main indicators identified in this research, we can mention financial indicators, technical indicators, risk indicators, environmental indicators and political-social indicators; Each of these indicators has sub-criteria. As a case study, we rated four dam construction projects using this decision-making method. The sensitivity analysis of the ranking based on changing the weights of the sub-criteria showed that the ranking has high robustness. The results show that this method can be used to select projects of the same type in other project portfolios.

Abstract In recent years, the significant increase in energy consumption and global warming have raised international concerns. Given the interconnectedness of economics, energy, and environmental concerns, energy consumption is critical in planning various systems. Optimizing production operations in various industries is a significant and complex challenge. Given the increasing global market competition and the importance of cost reduction, production process optimization has become increasingly important. One critical issue in this area is job scheduling in production systems with parallel machines. These systems' machine performance and energy consumption differences can significantly impact operating costs and job delivery times. These differences lead to machine heterogeneity, which is observed in many modern industries. Considering the challenges in managing energy consumption and the negative impacts of delays in product delivery, optimizing production processes to increase system efficiency and reduce energy consumption has become increasingly important. This research investigated the job scheduling problem in production systems with a heterogeneous parallel machine environment to minimize energy consumption and total job tardiness. In this research, a two-objective mathematical model for job scheduling was first designed, and a multi-objective meta-heuristic algorithm based on decomposition was used to solve this model. It was simulated in MATLAB software on several small, medium, and large sample examples. Comparing the results of the proposed method with those of previous methods shows the efficiency and superiority of the proposed method.

Abstract The ever-increasing volume and number of transactions in the bank make the fraud monitoring and detection process very complicated, costly, and time-consuming. In recent years, the development of new technologies has opened many ways for fraudsters and criminals to commit fraud. In this research, data mining methods are investigated in order to detect fraud in bank transactions. In order to detect fraud in bank card transactions, which are very unbalanced data types, the optimization of the support vector machine algorithm with hyperparameter techniques is presented and simulated on the Kaggle website data set, which includes bank card transactions, in the Python software environment has taken. The presented model benefits from bank transaction data and has the ability to extract complex patterns. As an effective optimization method, grid search technique intelligently adjusts the parameters of the support vector machine algorithm. The results of the model evaluation show that the support vector machine has a significant improvement in the detection of fraud patterns according to the criteria of accuracy and correctness. The combination of support vector machine and grid search technique as an innovative solution can help to improve the security of bank transactions in the digital age. In this research, hyperparameter optimization and smote balancing methods were used to reduce the number of false alarms. The proposed model can be commercialized and connected to the electronic banking system, online or offline, to detect fraudulent actions in transactions. The proposed model can be commercialized and connected to the electronic banking system, online or offline, to detect fraudulent actions in transaction.

Abstract In the landscape of healthcare, ensuring the accuracy and security of prescription processes is crucial for maintaining patient safety and upholding ethical standards. This paper presents a novel biometric authentication framework designed to address the vulnerabilities in traditional authentication methods such as passwords and codes, which are prone to misuse. By integrating fingerprint and iris recognition, the proposed multi-modal system provides a robust solution to prevent unauthorized access to prescription data. This study collected biometric data from 600 doctors, comprising 600 fingerprint images and 1200 iris images, to rigorously evaluate the system’s performance. Detailed information about the CNN architecture, including layers, activation functions, and loss functions, is provided. The model's effectiveness was measured using comprehensive metrics such as accuracy, precision, recall, and F1-Score, demonstrating a significant improvement over existing methods. Furthermore, a statistical analysis was conducted to verify the reliability of the results, with comparisons drawn against baseline methods. The findings underscore the importance of enhancing biometric authentication systems and contribute to the development of secure and reliable identity verification solutions across the healthcare sector. This research not only bolsters the security of prescription processes but also reinforces the ethical principles guiding medical practice, offering a significant step forward in preventing fraud in healthcare systems.

Abstract The issue of competitiveness has been considered by many researchers in recent decades, because it has become one of the most strategic and fundamental issues of business organizations, because it is the basis of survival and sustainability of the organization; In this regard, in accordance with the purpose and problem, the main strategy of the research is methodological pluralism by using simultaneously the qualitative method of thematic analysis and the quantitative method of structural-interpretive modeling in sequence. In the qualitative part of the research population, there were 15 experts were extracted 138 basic themes, 37 organizing themes and 14 global themes, which is the basis of quantitative analysis. The sample of the research in the quantitative part was the opinions of 11 managers of private banks in the country. The final model consists of 6 levels; In the first level of business model innovation, the most influential component and in the sixth level, market share, internal performance and international performance are the most influential components of competitiveness. Influence and dependency analysis using MICMAC shows that the criteria of organizational culture, human resource management and business model innovation are low dependency and high leadership; External stakeholder management criteria, internal performance, bank attractiveness and market share, these variables have strong dependence and poor guidance, and knowledge management criteria and business intelligence They have the nature of interface and dependency criteria.

Abstract Nowadays, financing is one of the fundamental challenges facing economic enterprises, influencing all organizational activities related to product manufacturing and service provision. Given the importance of the steel industry as the second-largest non-oil export sector in the country—alongside the threats and opportunities in global trade—assessing an organization's ability to manage risks in this field is of undeniable significance. Effective financing methods play a crucial role in sustaining operations, executing profitable projects, and ensuring companies' survival in today's competitive landscape. This study aims to identify and rank risk control factors in financing the steel industry, with a focus on Esfarayen Steel Company. The research follows a descriptive, survey-based, and applied methodology. Experts and specialists in the steel sector were consulted to identify key risk control factors, and their rankings were determined using the Delphi method and pairwise comparisons. Data analysis was conducted using SPSS (version 23) and Expert Choice (version 11). The results indicate that exchange rate risk is the most critical factor, followed by sanctions, export reduction, interest rate fluctuations, market recession, economic instability, and bankruptcy risk. Sharia compliance risk was ranked the lowest. The study suggests risk mitigation as the most effective approach for managing exchange rate risk and provides further recommendations for addressing other financial risks.