https://sjie.journals.sharif.edu/ Sharif Journal of Industrial Engineering & Management en daily 1 Mon, 22 Sep 2025 00:00:00 +0330 Mon, 22 Sep 2025 00:00:00 +0330 https://sjie.journals.sharif.edu/article_23977.html - https://sjie.journals.sharif.edu/article_23643.html The purpose of this study is to investigate the impact of marketing capabilities on export performance by considering the role of competitive advantage in food industry export companies. The research is classified as applied from the point of view of purpose and descriptive from the point of view of method and nature. Most of the previous studies in the field of export performance have examined the relationship between capabilities and performance, but not much research has been done on marketing capabilities and their impact on competitive advantage. Another category that plays an important role in export performance and is closely related to marketing communications is distribution channels in foreign markets. Based on this, deciding on the method of transferring the goods to the place of purchase or consumption is one of the important decisions faced by managers of export companies; Because finding a suitable distribution channel in international markets is so complicated. In this study, the statistical population is food industry export companies in Tehran province and the respondents to the questionnaire are CEOs and senior managers of the commercial and export departments of these companies. After collecting the questionnaires, the data is analyzed by SPSS, and SmartPLS softwares. The validity of the questionnaire has been examined through content and structure validity, as well as its reliability through Cronbach's alpha test and composite reliability. The results show that the questions have high reliability. In terms of structure validity, which was analyzed by SmartPLS software, it is determined that all the questions have good validity. Finally, the results of data analysis and hypothesis testing show that: marketing intelligence, pricing, marketing communications, distribution, and innovation have an impact on competitive advantage. Also, the results show that competitive advantage affects export performance. Meanwhile, the mediating role of competitive advantage in the relationship between the mentioned components and export performance is confirmed. According to the relationships between research variables, better planning can be done to improve export performance and gain a competitive advantage. https://sjie.journals.sharif.edu/article_23646.html Increasing profitability and reducing risk always requires choosing a smart investment path while taking advantage of data analysis; Therefore, it is necessary to provide a technique in the form of a decision support system for stock portfolio management while better understanding the position of data science. In this research, while combining data science methods with the Markowitz model, classification and forecasting models have also been created. Detecting financial fraud of companies is also effective; Hierarchical and Cummins algorithms have also been used in order to cluster active companies in the Tehran Stock Exchange. In terms of data classification, the linear support vector machine classification algorithm has been identified with 70% accuracy in comparison with the decision tree, Nyobies, nearest neighbour, and multilayer perceptron algorithms, and in terms of building prediction models, the decision tree with the minimum amount of error, in comparison with Nyobies algorithms, is the closest Neighbor, multilayer perceptron has been identified. The primary data in the current research includes twenty titles of financial indicators and daily data of stock prices of companies in the Python programming space. It was concluded that choosing stocks from among the clusters formed by different stocks of companies will reduce the risk of diversifying the stock portfolio. The mentioned system will be able to generalize as a comprehensive model for data in different time intervals and the indicators desired by the analyst, thus helping investors in a fast and accurate analytical way. The performance of the final technique will be such that the investor first selects several desired companies according to the heard, analysis, and news; It predicts the price performance of companies; Then, it separates the companies that have succeeded in accepting the condition by using the clustering model and separates the portfolio of various stocks. It forms according to the amount of risk and expected return. Importantly, as mentioned, the presented classification and forecasting models, in addition to being used in the formation and management of the stock portfolio, will be able to be effective in predicting the possible fraud of companies. https://sjie.journals.sharif.edu/article_23748.html Optimum location for the establishment of mobile phone towers with the aim of covering all demand with high signal quality and low cost is an important challenge for telecommunications companies. Therefore, if a proper decision is not taken, it may lead to environmental damage, economic problems, and user dissatisfaction. Therefore, operators of telecommunication networks should plan for the optimal location of mobile telephone towers in order to achieve the goals of sustainable social, economic, and environmental development. The purpose of this research is to show how to use ArcGis10.8 software tools and the best-worst technique for the optimal location of mobile phone towers using sustainable development criteria. These criteria are: distance from the fault, distance from historical and cultural places, distance from parks, distance from the river, distance from medical centers (hospitals and clinics), distance or proximity from rural areas, proximity to the main road, distance from residential areas, distance from schools and educational centers, height, land slope, and slope direction. The innovations used in this research include the use of multi-criteria spatial modeling, including the best-worst technique and its integration with geographic information system techniques for locating mobile phone towers and investigating the optimal location for their deployment. along with the application of new criteria of sustainable development, which was done for the first time in Urmia city. The weights of the criteria were obtained by the best-worst technique, and the data required for weighting were collected by interviewing experts who were experts of the telecommunications company. The data related to each quality was entered into the ArcGis10.8 software, and various processing and fuzzy overlay operations were performed to prepare the optimal final map. The general results obtained showed that it is not possible to use the masts in a wide area from the west of the county, and the best points go towards the eastern areas near Lake Urmia, which can be due to the selection of the criteria. https://sjie.journals.sharif.edu/article_23648.html In recent decades, the rapid and remarkable advancements in technology have sparked significant interest in the potential of big data and the extraction of valuable insights and information from it. This has led to a surge in the utilization of big data analytics across various industries, including supply chain management. In this paper, our focus is on evaluating the dynamics of a three-level supply chain, which encompasses a retailer, manufacturer, and supplier, within different power structures. Notably, the members of this supply chain have made substantial investments in big data analytics and are reaping the benefits of these investments accordingly. In our quest to gain a deeper understanding of the feasibility conditions and factors that impact big data investment within different power structures, we have meticulously examined the problem model in two distinct cases: one with big data investment and another without it. Our findings reveal that the direct impact of investment efficiency and cost improvement coefficient plays a significant role in determining the feasible limits of big data investment for all members of the supply chain. Furthermore, our research demonstrates that big data investment has a positive and far-reaching effect on the equilibrium profit of members. This leads to an increase in the equilibrium price of products, as well as an enhancement in the equilibrium level of green innovation and product quality across all power structures. https://sjie.journals.sharif.edu/article_23783.html Clustering of customers and analysis of their lifetime value is one of the basic strategies of policy making in production and marketing. Since the advertising strategies of many businesses are implemented regardless of the financial behavior clusters of customers and the expected income resulting from the dynamic financial behavior of customers, the advertising policy based on the customers’ lifetime value in the dynamic clusters of their financial behavior can save a lot of advertising and marketing costs to manage the costs imposed on businesses. This is despite the fact that the studies conducted in the field of customer lifetime value analysis have not paid attention to advertising strategies based on lifetime value. The existing methods for determining the lifetime value of customers rely on static clustering of customers' status, while customer clustering based on their financial behavior is a dynamic and time-varying phenomenon. Therefore, in this research, the lifetime value analysis of customers and the optimization of advertising strategy in the dynamic clusters of their financial behavior using a self-organizing neural network and dynamic stochastic programming have been discussed. For this purpose, based on the status of each customer belonging to a cluster during consecutive weeks, a Markov chain is formed from the clusters containing customers, and the lifetime value of customers is based on the transition probability matrices of customers' status from one cluster to another, and it is also estimated based on the expected income of customers in each cluster. The results showed that customer clustering with the self-organizing neural network method can explain at least 91% of the changes in the data. Also, the findings showed that the optimal advertising strategy for each cluster of customers with a certain expected income level will lead to different lifetime value for customers, which shows the importance of dynamic clustering of customers and determining the optimal level of advertising cost in each cluster to gain more customer lifetime value. https://sjie.journals.sharif.edu/article_23749.html Glaze is one of the fundamental components of ceramic products such as tiles, porcelain dishes, and sanitary ceramics. The formulation of glaze is a critical and complex process, representing one of the most significant and challenging aspects within the ceramic industry. Establishing the optimal formulation for glaze production is crucial for enhancing product quality and minimizing production costs. In this study, grounded in practical experiences from a factory setting, the issue of glaze formulation is examined and analyzed using an operations research approach for the first time globally. The outcomes of the developed model were tested and successfully implemented on the production line. To ensure the practical implementation of this research within the industry and to enhance user accessibility, a user-friendly software was developed. It is crucial to recognize that different factories employ a variety of raw materials in their glaze production processes. This variation arises from factors such as the unavailability of certain raw materials, the elevated costs associated with some materials, and the expenses related to transportation. The chemical composition of raw materials varies significantly, with the percentage of oxides differing based on the specific mine and the section of the mine from which they are sourced. Consequently, determining the optimal proportion of available raw materials for producing a glaze with a specific Seger formula necessitates repeated analysis. This iterative process is essential to identify the appropriate formula, which can then be implemented in the production line. In this study, conducted with real-world data and a thorough understanding of the problem through the production process, as well as an extensive review of relevant literature, the issue of designing a glaze formula is analyzed using operations research methodology. This paper presents a novel approach to glaze formulation, which can serve as a foundation for future research in this area. https://sjie.journals.sharif.edu/article_23750.html In the financial literature, the capital market plays an essential role in economic growth through the financing of enterprises, the optimal allocation of resources, the improvement of liquidity of assets, the improvement of company management, and the increase of transparency in the economy. One of the most important challenges that shareholders always face in the market is to make the right decision and be in the right position with buying and selling stocks. Stock forecasting that predicts future stock movements and benefits shareholders has been an attractive research area for financial studies and researchers for the past. In this research, we present two models of neural networks that receive inputs and predict price and movement trends, and finally, the performance of these two models is compared. The data studied in this research includes the price data of the 5 largest shares of the New York market during the years 2000 to 2020, in which 80 percent of the initial samples are used as training data and the remaining 20 percent are used as test data.In the proposed VMD-LSTM model, first, the stock price time series is decomposed using the variational mode decomposition algorithm (VMD) into the intrinsic mode functions (IMF), and then each of these IMFs is predicted by the LSTM model and After interpreting the results. In the second proposed method, available features, including price and some of the most important technical indicators, are used to predict stock prices. In the GA-LSTM model, the genetic algorithm is first used to select the best features from the entire set of features. Then, the time series of the stock closing price was predicted by an LSTM network using the selected features. The results of the research showed that because both models are very good in price prediction, the proposed GA-LSTM model, which is developed based on feature selection, has less error and more accurate performance. https://sjie.journals.sharif.edu/article_23753.html Vehicle routing problems are a category of complex mathematical problems in the field of transportation and supply chain management, and a wide variety of these problems have been introduced. One category within this field involves the collaboration of two different types of fleets in a network, where in many cases, one of the fleets must patrol the graph as a support fleet and is not subject to the same constraints as the other fleet, such as only visiting a vertex or an edge once. Examples include refueling fleets or trucks providing services to drones, where these fleets patrol the network so that the other fleet can use their services as needed. In this paper, the problem of the free movement of such fleets on a graph is examined as a free patrol problem, which can be used as a sub-problem in the modeling and solving of many routing problems. To this end, the problem is modeled as a mixed-integer linear programming (MILP) problem in both discrete and continuous time spaces. Generally, it is expected that the model in continuous time space will exhibit lower computational complexity, but since modeling in continuous time requires changes to the graph, leading to an increase in the number of vertices and edges, its behavior needs to be more thoroughly investigated. On the other hand, another challenge in analyzing this problem is that the objective of the free patrol sub-problem depends on the main problem, as without the main problem, the free patrol problem may lose its objective, with a fleet patrolling the graph within a designated time. Therefore, to decouple and make the free patrol independent of the main problem, five different objective functions have been introduced for each model, covering a wider range of comparisons, leading to ten models in total. Subsequently, all ten models were solved exactly using randomly generated data for ten different graphs. Finally, the behavior of the models was evaluated and compared from various perspectives. The numerical results demonstrate the superior performance of the continuous time model over the discrete time model. https://sjie.journals.sharif.edu/article_23694.html Since hardware may experience sudden failure and software solutions are often costly, users need an environment to perform computational and data storage tasks without expensive hardware and software. Cloud computing can provide this capability, but the presence of cyber hackers and their attacks raises user concerns about their data security. As information is precious, losing it can result in significant costs for the information owner. To address this problem, companies have emerged to ensure the safety of cloud computing services, and cloud users can entrust their information security to them. This article aims to examine the competition between security provider companies and cyber hackers using game theory and determine the strategies of each player to determine the game structure. These structures are based on the leader's decision to determine the security level initially or after an attack has occurred. The company decides what price to offer the user based on the value of the information, the amount of effort needed to return the information after a successful attack, the security level it needs to maintain, and the power structure. Similarly, the hacker decides how much effort to put in based on the value of the information. The results show that the price decreases linearly based on the information value when the company is the leader. In addition to the results obtained about the company's profit, it shows that in general, the company's profit, when it is a leader, is more than when it is a follower, and in particular, the company's profit based on the percentage of returned information in the leader's position is much higher than in the position of the follower. The level of security provided is also different according to the position of the company, and when the company is the leader, it is much higher than when the company is the follower, based on the hacker's credibility and the value of the returned information. https://sjie.journals.sharif.edu/article_23678.html The efficient transportation of goods and passengers from origin to destination is a crucial aspect of supply chain management. The design of transportation systems plays a key role in determining system costs and customer satisfaction. In cases where direct communication between all points is not feasible, the hub and spoke system can be utilized. The design of the hub network is a strategic decision that faces uncertainties such as demand, costs, and system reliability. Natural and unnatural events can impact the efficiency of hub facilities, leading to additional costs for the system. Capacity limitations in hub facilities may necessitate crisis management strategies, such as transferring flows to backup hubs during emergencies. This study explores the use of single backup and multiple backup approaches to address hub unavailability and meet demand requirements. A mathematical model is presented to investigate and solve the single and multiple backup strategies. Due to the complexity of the problem, a genetic algorithm approach is employed for optimization. The performance of the algorithms is evaluated using the CAB dataset, demonstrating the effectiveness of the proposed solutions. Comparing the results of single backup and multiple backup strategies reveals that the latter is more advantageous in terms of system costs and congestion in hub nodes. This study highlights the importance of strategic planning in transportation systems and the benefits of implementing backup solutions to ensure efficient operations. This research underscores the critical role that transportation systems play in the overall success of supply chains and the significant impact that effective logistics management can have on customer satisfaction and operational costs. The strategic decisions made in designing transportation networks can have far-reaching implications on the overall efficiency and reliability of the system. By exploring different backup strategies and utilizing a mathematical model, organizations can better prepare for disruptions and ensure continuity of operations. The use of genetic algorithms and data analysis tools can provide valuable insights into the performance of transportation systems and help identify opportunities for improvement. Overall, this study emphasizes the importance of proactive planning and the adoption of innovative solutions in the field of logistics and transportation management. https://sjie.journals.sharif.edu/article_23780.html Hospitals are one of the key parts of the health system that are responsible for providing services to patients. The optimal allocation of hospital beds is one of the important issues that plays a significant role in the financial and clinical performance of hospitals. Therefore, in this article, an algorithm in six main steps, including data collection, simulation, scenario definition, simulation model execution, calculation of the importance degree of output variables, and ranking of scenarios, has been developed using a computer simulation approach and considering the bed-sharing policy among different hospital departments. The main criteria considered in this study include patient rejection (PR), the percentage of resource utilization (RU), and the length of the queue (LQ). Due to the nature of uncertainty in the problem, a fuzzy DEMATEL method has been used for ranking scenarios. Finally, the best scenarios have been identified from the total scenarios considered, which can be taken into account by hospital managers in decision-making to improve the overall performance of their medical unit using optimal scenario assumptions. The presented algorithm has been investigated on a case study, and its results have been analyzed and reviewed. The considered case study hospital has two inpatient departments. The first department is designated for triage patients and first-type clinic patients, while the second department is for second-type clinic patients. In the conducted case study, the weights of each decision criterion are 0.33, 0.09, 0.27, and 0.30, respectively. A total of 36 scenarios have been defined for the case study. In the end, based on the developed model, scenario number 20 has been chosen as the best scenario. In this scenario, the percentage of patient rejection for triage is 9/5%, the resource utilization percentage is 70%, and the average number of people waiting in line for first and second-type clinic patients is 19.73 and 1.27, respectively. https://sjie.journals.sharif.edu/article_23978.html - https://sjie.journals.sharif.edu/article_23894.html This study presents a novel three-phase hybrid approach for sustainable supplier selection and order allocation, integrating Multi-Criteria Decision-Making (MCDM), machine learning, and mathematical programming techniques. In the first phase, the Grey-TOPSIS method is employed to evaluate suppliers based on economic, environmental, and social criteria, effectively handling uncertainty in expert judgments. In the second phase, a machine learning model based on Support Vector Regression (SVR) is developed to calculate suppliers' final sustainability scores. This model enhances consistency and scalability by leveraging the initial evaluations and learning from historical data. In the third phase, the predicted scores are incorporated into a multi-objective mixed-integer programming model to determine the optimal supplier selection and order allocation. The model considers practical constraints such as supplier capacity, transportation modes, and risk mitigation through order splitting. The key innovation of this study is the sequential integration of these three important techniques for the first time. With the SVR method, once new suppliers are introduced, it is not necessary to re-apply the MCDM method, as the SVR model learned can predict sustainability scores. Additional innovations include the application of uncertainty through grey number theory in the initial phase and the possibility of obtaining financial support, as well as the need to limit the supply of each good from each supplier to reduce risk in the mathematical model during the third phase. To validate the proposed approach, two evaluations were conducted. First, the impact of the SVR model was assessed by comparing the results with a scenario using only Grey-TOPSIS scores. Incorporating SVR led to a 1.76% improvement in the weighted goal programming objective. Second, the proposed model was compared with four company-specific policies reflecting current operational practices. The results showed average improvements of 0.67% in sustainable procurement, 5.57% in operational cost, and 2.9% in environmental cost, resulting in an overall 5.61% enhancement in the weighted objective function. These findings demonstrate the practical effectiveness of the proposed framework in delivering balanced sustainability outcomes in real-world supply chain decision-making. https://sjie.journals.sharif.edu/article_24004.html The day-by-day increasing need for the generation and distribution of electric power all over the world creates the power market restructuring. Virtual power plants as a set of supply and demand units have a growing role in the power markets. The demand management as a new concept in the energy supply chain helps to improve the network reliability, reduce the operational costs, protect the environment, and increase the consumer satisfaction. In this research, MILP and machine learning approaches are proposed for the optimal unit commitment in a virtual power plant using a demand-side storage to reduce the gap between energy supply and demand. The proposed model has two objective functions: Maximizing the profits and minimizing the energy loss of hydroelectric power plant as well as the operational costs of pumps. Multiple scenarios based on the demand-sid storage, weather conditions, network grid status is studied. The numerical results for the IEEE standard 24-bus system show that although the use of demand-side storage increases the related cost as well as the generation fluctuation in the case of virtual power plant, the related costs are about 1.5 times lower than those of the traditional power plant without storage. In addition, comparing the traditional grid with a virtual power plant shows a 1.7-fold reduction in costs. However, comparing a virtual power plant including demand-side storage with a virtual power plant without demand-side storage shows an increase in costs along with an increase in energy production fluctuations. To solve the model in the real-world dimensions, a ELM-GA hybrid algorithm is proposed which provides a near optimal solution but much faster than the MILP method because the training and learning accelerate the convergence to the optimal point. The experimental results for a large-sized problem of 60 units demonstrate the small optimality gap of about 4.4% with the MILP but 16 times superiority in the solution time. https://sjie.journals.sharif.edu/article_24036.html In this study, a two-stage mathematical model is proposed for scheduling football league competitions with the primary goal of organizing matches in a fair and orderly manner. In the first stage, the sequence of matches, the assignment of home and away teams, and the allocation of referees are determined by considering various constraints to ensure justice in the competition. These constraints include alternating home and away match for each team, preventing a weak team from playing against two strong teams in consecutive weeks, and others aimed at promoting fairness. Subsequently, in the second stage, the exact scheduling of each match, specifying the day and time, is determined based on the outcomes of the first stage. The main innovation of this research is the stepwise decision-making process, which not only separates the determination of match sequence and exact timing but also incorporates practical constraints such as referee experience level, balancing the number of games officiated by each referee, and ensuring a minimum three-day interval between games for any team. Using the proposed model, it is possible to develop an optimal schedule for up to 6 teams and 8 referees over a 10-week period, and to conduct comprehensive analysis of the results. The results of the sensitivity analysis on the number of strong and weak referees indicate that when the number of both groups is equal, the smallest deviation from fairness-oriented criteria is observed. As the disparity between the sizes of these two groups increases (assuming the total number of referees remains constant), the deviation also increases. Overall, this model offers a practical and effective solution for fair scheduling and referee assignment in football leagues by integrating real-world requirements and fairness criteria into a comprehensive optimization framework, which can be adapted to various league sizes and complexities to ensure balanced and equitable competition. https://sjie.journals.sharif.edu/article_24039.html The rapid growth and diversification of smart devices and gaming consoles have driven a significant global expansion of the video game industry, resulting in a dramatic rise in the number of players and the complexity of its supply chains. Within these digital supply chains, information asymmetry remains one of the most critical challenges for both game developers and platforms, affecting decisions on pricing, quality, and investment. Traditional wholesale contracts have often been employed, yet the increasing prevalence of agency pricing models has highlighted the need for more nuanced approaches. Emerging technologies, particularly blockchain, alongside platform-based business models, play a transformative role in enhancing transparency, reducing opportunistic behavior, and reshaping the economics of the industry.This study investigates a digital game supply chain consisting of a developer and a platform, where decisions on price, game quality, and promotional effort are analyzed under asymmetric information. To capture the hierarchical nature of this interaction, we employ a Stackelberg game framework, designating the platform as the leader and the developer as the follower. The model is examined under two scenarios: the absence of blockchain and the presence of blockchain. By comparing these scenarios, the research evaluates how blockchain adoption influences decision-making, bargaining power, and overall supply chain performance.Numerical analysis reveals that blockchain implementation reduces information asymmetry by enabling developers to access more accurate demand information. This transparency allows developers to set higher-quality standards and more informed prices, while platforms increase their promotional efforts. Consequently, blockchain adoption enhances the overall efficiency of the supply chain, improves end-user satisfaction, and strengthens the developer’s bargaining position, which in turn lowers the revenue-sharing rate demanded by the platform. The findings contribute to both theory and practice by integrating agency pricing, Stackelberg competition, and blockchain into a unified framework for video game supply chains. Beyond offering managerial insights for developers and platforms, the study emphasizes blockchain’s potential as a strategic tool for mitigating risks associated with information asymmetry and fostering sustainable growth in the digital gaming industry. https://sjie.journals.sharif.edu/article_24045.html Enhancing the reliability of repairable systems plays a crucial role in improving operational efficiency and reducing maintenance costs across various industries. Due to their functional complexity and exposure to diverse environmental and operational conditions, these systems are prone to frequent failures. Therefore, developing optimized methodologies for experimental design and reliability analysis is essential.This study presents an innovative approach that integrates the Taguchi design with the I-optimal design to identify optimal operational conditions, minimize failure rates, and improve system reliability. The Taguchi method was first employed as an effective tool to reduce sensitivity to noise and enhance the robustness of experimental results. Its integration with the I-optimal design further enabled the identification of the best factor level combinations while reducing the number of required experiments. The efficiency and information richness of the resulting design were subsequently evaluated using the D-optimality criterion, which demonstrated high design performance.Given the limited access to real-world failure data, time-to-failure data were generated through predictive modeling and simulation to evaluate the proposed methodology. For data analysis, parametric survival models were employed, providing accurate representations of system failure behavior and enabling the investigation of interaction effects among multiple factors.The findings of this study revealed that integrating Taguchi with I-optimal design, followed by evaluation with the D-optimality criterion, significantly improved model accuracy while reducing experimental effort. Moreover, the proposed approach increased system resistance to environmental variations, thereby extending time-to-failure and enhancing overall reliability metrics.By combining advanced experimental design techniques with robust statistical modeling approaches, this research provides a systematic and practical framework for optimizing the reliability of repairable systems. The results highlight the effectiveness of customized experimental designs in reducing failure rates, improving operational stability, and strengthening system robustness. This study represents an important step toward more efficient reliability optimization methodologies and offers valuable insights for industries such as manufacturing, energy, and transportation, enabling enhanced system performance and longevity with minimized maintenance costs and operational disruptions. https://sjie.journals.sharif.edu/article_24047.html With the rapid advancement of technology and the increasing consumption of perishable or obsolete-prone products such as electronic devices, pharmaceuticals, and medical equipment, inventory control for these items has become more critical than ever. Proper inventory management of such items not only prevents excessive costs but also significantly reduces waste and environmental hazards. This study presents an inventory control model for perishable items that simultaneously incorporates three key factors: all-units quantity discount, the probability of sudden obsolescence, and holding cost dependent on purchase cost. In the proposed model, suppliers offer discounts based on order quantity thresholds, and the holding cost is defined as a function of the item's purchase cost. The model is designed to assist decision-makers in balancing order quantity, financial costs, and obsolescence risks under real-world conditions.The main objective is to determine the optimal economic order quantity (EOQ) that minimizes the total inventory cost while avoiding stockouts and accounting for the risk of obsolescence. To achieve this, a mathematical formulation of purchasing, ordering, holding, and obsolescence costs was developed. The convexity of the objective function was proven using first- and second-order derivatives, and graphical analysis. A numerical example using real-world data was provided to evaluate model performance, and a sensitivity analysis was conducted to examine the impact of key parameters. In addition, the model’s stability was analyzed under different economic scenarios.The results show that increasing ordering costs leads to higher order quantities, which in turn reduces purchasing costs due to quantity discounts. Moreover, increasing the purchase elasticity coefficient reduces the order quantity, resulting in lower total inventory costs. On the other hand, increasing the holding cost coefficient or obsolescence-related costs can significantly raise total system costs. Overall, the model enables a better understanding of how price-dependent costs and obsolescence risks interact, helping organizations optimize their inventory strategies. The proposed approach can serve as an effective tool for inventory decision-making in environments dealing with time-sensitive or rapidly outdated items. https://sjie.journals.sharif.edu/article_24053.html Nowadays, due to globalization and intense competition, manufacturers face numerous challenges in maintaining their competitive advantages. They strive not only to reduce costs but also to increase system profitability. In production systems, due to increasing wear over time and machine aging (such as corrosion, deterioration, and breakage), many components experience failures. Therefore, the simultaneous optimization of maintenance and production planning has always been a subject of interest for many researchers. The production process involves various factors, including production and demand rates, shortage cost rates, and the planned time horizon, which significantly impact maintenance decision-making and vice versa. Moreover, the multi-component and complex nature of production systems cannot be overlooked. Thus, in this research, a simultaneous optimization model for maintenance and production planning of multi-component systems has been developed, considering safety stock and shortages, under a condition-based maintenance policy. The system produces a single type of product to meet constant demand within a finite time horizon. After each production cycle, inspections are conducted to determine the condition of components, and maintenance is performed if necessary. Since component degradation does not lead to immediate system stoppage but negatively affects performance, a criterion called structural importance has been utilized for component prioritization. Additionally, this study assumes that both preventive and corrective maintenance are carried out completely, and condition-based maintenance activities are based on preventive reliability and structural dependency of components. Inventory shortages are allowed and are considered as backorders. The objective of the model is to minimize the average cost rate, including: setup costs inspection costs, preventive and corrective maintenance costs, inventory holding costs, safety stock holding costs, replenishment costs and shortage costs. The model determines the optimal values of decision variables, including optimal production quantity, shortage amount, and preventive maintenance threshold. Finally, the proposed stochastic mathematical model is solved using a simulation-based optimization approach with Monte Carlo simulation. https://sjie.journals.sharif.edu/article_24054.html Capacity planning in continuous manufacturing industries faces significant challenges due to the demand uncertainty, cycle time variability, and unplanned downtime occurrences. Traditional planning methods based on the nominal equipment capacity fail to provide realistic schedules, leading to production target deviations, increased operational costs, and reduced customer satisfaction. This research develops a model combining machine learning techniques and mathematical optimization for capacity planning. The proposed framework consists of four main components: demand forecasting using Random Forest algorithm, cycle time estimation (ideal and actual) employing Random Forest algorithm, downtime pattern analysis through moving average methodology, and bi-objective capacity optimization aimed at maximizing productivity while minimizing overtime costs. The Random Forest algorithm was selected for its superior performance in handling complex, non-linear relationships and its robustness against overfitting. For demand forecasting, historical production data spanning four years was preprocessed through standardization and feature encoding techniques. The cycle time approach distinguishes between ideal conditions (effective time/output ratio) and realistic scenarios (total shift time/output ratio), providing comprehensive insights into production capabilities. The downtime analysis component utilizes moving average techniques to identify recurring patterns and predict failure probabilities, enabling proactive maintenance scheduling. The optimization module formulates a bi-objective mathematical model that balances normal capacity utilization maximization with overtime cost minimization, subject to demand fulfillment, capacity constraints, and resource availability limitations. The model incorporates capacity transfer capabilities between months, allowing efficient resource allocation throughout the planning horizon. Model validation was conducted in a heating radiator manufacturing facility using four years of production data. Implementation results demonstrated the effectiveness of the proposed approach, achieving a 34% reduction in downtime, 25% increase in productivity, and 106% achievement of annual planning targets. The integrated methodology successfully bridges the gap between theoretical capacity and practical production requirements, providing manufacturers with a robust tool for strategic capacity planning. The study contributes to the manufacturing optimization literature by presenting a novel combination of ensemble learning and mathematical programming techniques specifically tailored for continuous production environments. https://sjie.journals.sharif.edu/article_24084.html The intricate nature of material transportation systems in extensive open-pit mines presents significant challenges for conventional planning methodologies. These traditional approaches often fall short in effectively managing the dynamic interplay between large fleets of trucks and shovels, navigating complex routes, and adapting to ever-changing operational conditions. Consequently, this can lead to suboptimal resource allocation, decreased productivity, and increased operational expenditures. To overcome these limitations and boost productivity, a simulation system leveraging the power of reinforcement learning (RL) algorithms has been developed to optimize the crucial aspects of shovel and truck allocation and scheduling within open-pit mining environments. By employing RL, an intelligent agent undergoes a training process to make informed decisions regarding the most efficient vehicle movement pathways, establish precise schedules for loading and unloading operations, and strategically allocate available resources across the mine site. This agent learns through continuous interaction with a simulated mine environment, receiving feedback on its actions to progressively refine its decision-making policies.The findings of this research underscore the effectiveness of the implemented RL model, which utilizes the Q-learning algorithm as its core learning mechanism. The model demonstrated a significant ability to learn the optimal assignment of trucks to shovels, consistently outperforming simpler, more conventional models in terms of efficiency and productivity. The success of the Q-learning approach lies in its iterative process of updating Q-values – which represent the expected future reward for taking a specific action in a particular state and its capacity to learn from the accumulated experience gained through simulated operations. Through this continuous learning and adaptation, the RL model gradually converges towards an optimal operational policy. This optimized policy results in a higher frequency of successful material downloads, minimizing idle times and bottlenecks within the transportation network. Ultimately, this leads to a substantial enhancement in the overall efficiency of the mine's transportation system. The implications of this research highlight the transformative potential of integrating reinforcement learning techniques into the planning and management of mining operations, paving the way for the development and deployment of more intelligent, adaptive, and ultimately more productive autonomous and semi-autonomous systems in the mining sector. https://sjie.journals.sharif.edu/article_24096.html Agricultural supply chains face mounting challenges in balancing economic efficiency with social and environmental sustainability, particularly under conditions of supply and price uncertainty. Jujube is a strategic horticultural product in Iran, but its current supply chain suffers from high waste, weak coordination, and lost value-adding opportunities. This paper presents a multi-objective mixed-integer linear programming model for designing a sustainable closed-loop jujube supply chain based on circular economy principles. The model simultaneously minimizes total cost, maximizes job creation, and improves responsiveness. Responsiveness is measured through the reliability of processing and distribution facilities, so the model selects more reliable nodes when it allocates flows. The environmental dimension is improved by converting all agricultural residues and processing waste into biochar and by imposing a strict CO₂ emission cap on all echelons of the network. Uncertainty in orchard capacity and purchase prices, and their inverse relationship, is modeled by a two-stage stochastic programming approach with optimistic, realistic, and pessimistic scenarios. The proposed framework is applied to a real case study in South Khorasan Province, which produces about 98% of Iran’s jujube. The network includes several types of processing facilities and multiple domestic market segments. The stochastic multi-objective model is solved by the AUGMECON2 method in order to generate the complete Pareto front and reveal the trade-offs among cost, employment, and responsiveness. Results show that the integrated design can significantly reduce total cost while increasing both job opportunities and environmental performance compared with current fragmented practices. The analysis of Pareto solutions highlights the high social and service-level cost of aggressive cost minimization and helps decision-makers select balanced policies. Sensitivity analyses on demand, shortage cost, transport cost, and the carbon cap, as well as on the presence of the biochar facility, confirm that the results are robust. These tests show that the model is practically useful for strategic decision support in agricultural supply chains under uncertainty. https://sjie.journals.sharif.edu/article_24116.html An unbalanced healthcare facility system causes patients to travel between areas to reach qualified treatment resources, resulting in patient fatigue, unnecessary expenses, delays in disease diagnosis and treatment, and environmental destruction due to CO2 emission. The environmentally sustainable healthcare facility location-allocation problem focusing on minimizing the carbon footprint effect resulting from patients’ movement has not been considered. Cancer hospitals provide cancer patients with comprehensive treatment services helpful for avoiding cancer patients referring to different healthcare centers. Child cancer hospitals with a child-centered design can facilitate cancer disease curing period for children. Although cancer hospital network design has been noticed in recent years, the child cancer hospital location- allocation problem has received no attention. This paper contributes to the environmentally sustainable healthcare facility location-allocation problem by introducing a multi-objective, mixed-integer linear mathematical model to minimize hospital establishment costs, surplus established beds, and the carbon footprint effect for child cancer hospitals' network design. The newly developed model is solved in a case study to propose a child cancer network establishment for Iran in 2030 using the CPLEX solver due to the significant shortage of specialized cancer treatment capacities in cancer hospitals, particularly those for children. The results indicated the necessity of establishing new child cancer hospitals in 9 provinces, with the highest number of new beds required in Isfahan province and existing beds in Tehran province. The validation of the carbon footprint effect demonstrates that reducing the carbon footprint could lead to a 50% decrease in CO2 emissions. Additionally, the sensitivity analysis reveals that overlooking certain cost and efficiency factors could significantly reduce the carbon footprint effect, highlighting the potential for improved healthcare equity in cancer treatment by reducing the need for child cancer patients and their families to travel between provinces. In addition, several managerial insights and recommendations for future research are proposed. https://sjie.journals.sharif.edu/article_24119.html Given that modeling and predicting customer behavior using data science helps companies gain a better understanding of customer behavior, this research focuses on analyzing customer reviews in the women’s clothing domain within e-commerce. We employ machine learning techniques and natural language processing (NLP) to achieve this goal. The machine learning models used include Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, Multinomial Naive Bayes, Complement Naive Bayes, XGBoost, and LightGBM. To extract and vectorize text features from the reviews, we utilize the TF-IDF and Word2vec algorithms. We employ Topic Modeling using Latent Dirichlet Allocation (LDA) method and k-means clustering. The dataset consists of women’s clothing reviews, with the target variable being customer ratings in those reviews. The study is conducted in binary, three-class, and five-class scenarios. The target variable, which originally has five classes (scores 1 to 5), is categorized into two-class and three-class modes. In the two-class mode, scores below 3 are class zero, while scores of 3 and above are class one. In the three-class mode, scores below 3 are class zero, scores equal to 3 are class one, and scores above 3 are class two. In all three cases, the Random Forest model performs best, achieving an accuracy of 0.98 in the binary case, 0.95 in the three-class case, and 0.91 in the five-class case. After performing the required preprocessing and feature engineering, principal component analysis (PCA) and T SNE are applied. After that, the scatter diagram of the data is drawn and the optimal number of clusters 3 is estimated using the ELbow diagram. In the next step, by removing punctuation marks, stop words and words with less than three letters, converting the first letter of the words to lowercase and lemmatization, data cleaning was done. After that, topic modeling is done and each of the topics and words related to them are examined. In the next step, the topics are examined in different clusters. These analyzes provide a comprehensive understanding of the key themes and concerns customers have when considering womenswear items in each of the four topics. https://sjie.journals.sharif.edu/article_24120.html In this research, a system dynamics approach has been employed to model and simulate the structural dynamics of capacity development for power transmission, specifically focusing on substations operating at 400, 230, 232, and 66-63 kilovolts. The model takes into account the exogenous factors of electricity demand and supply. This study reviews previous domestic and international research, providing a suitable framework for analyzing investment policies in the transmission industry. The diagram of subsystems incorporates variables such as inflation, electricity demand, and electricity supply in an exogenous manner. In the causal loop diagram, key variables including existing transmission capacity, equipment depreciation, equipment age, maintenance costs, transmission network losses, and transformer failure probability are considered, with both direct and indirect relationships among them modeled accordingly. After establishing the stock and flow diagram model, mathematical relationships between the model variables were incorporated. Various conventional validation methods for system dynamics models were utilized to ascertain the model's credibility, including boundary tests, structural evaluation tests, and behavioral reproduction tests. The results from the behavioral reproduction test indicated that the model accurately simulated the past 40 years of transmission capacity with over 90% accuracy. Upon validating the model, three scenarios were simulated within this domain. The outcomes reveal that in the second scenario, where electricity demand is optimistically projected to increase due to savings or other changes over a 20-year horizon, the annual construction of transmission capacity calculated by the model results in the smallest gap between required and actual capacity. Conversely, in the third scenario, where both electricity demand increases and annual capacity construction continues based on a previous five-year average, the largest discrepancy between required and actual transmission capacity is expected by the year 1420. The numerical results from these three scenarios concerning the construction of transmission capacity by substation type are presented in this paper. https://sjie.journals.sharif.edu/article_24135.html In today's industrial landscape, enhancing productivity in manufacturing sectors, particularly in automated continuous industries, has gained paramount importance. This paper introduces an innovative data-driven reward and penalty system designed to boost productivity. The proposed approach targets agile and team-based performance evaluation, focusing on key indicators such as downtime adjustments, stoppages, rework, and process non-conformities. This methodology is founded on systems thinking and a team-oriented approach, aiming to create a holistic view of operational efficiency.The core of this system lies in its unique integration of statistical methods, specifically entropy and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) techniques. This combination allows for dynamic weighting of performance indicators in each time period, enabling rapid identification of performance fluctuations and highlighting areas requiring process improvement or correction. By employing this data-driven approach, the system provides a responsive and adaptive framework for continuous improvement.To validate its effectiveness, the system was implemented in a heating radiator manufacturing plant. The implementation process was carefully structured, with an initial three-month period followed by a three-month validation phase. The system's direct impact on human capital was evident, fostering team cohesion and promoting a collective mindset among employees.The results of this implementation were significant and multifaceted. After the six-month trial period, the plant witnessed a remarkable 22% increase in production output. This substantial boost in productivity was accompanied by a 20% reduction in failure rates, indicating improved process reliability and efficiency. Furthermore, the rate of defective products decreased by 1.5%, reflecting enhanced quality control measures. Perhaps most notably, the plant's process audit score saw a significant improvement of 21.5%, demonstrating a comprehensive enhancement in overall operational standards.These impressive outcomes underscore the effectiveness of the proposed system in driving tangible improvements across multiple facets of manufacturing operations. The success of this implementation not only validates the theoretical framework but also provides a practical blueprint for other industries seeking to optimize their productivity and quality metrics. By leveraging data-driven insights and fostering a team-oriented culture, this innovative approach presents a promising solution for manufacturers aiming to stay competitive in an increasingly demanding industrial landscape. https://sjie.journals.sharif.edu/article_24138.html This article examines the waste management system to assess the feasibility of constructing a waste incineration power plant. Due to the haphazard burial of waste daily in the Saravan region of Rasht, without adherence to proper burial engineering principles, there are several potential environmental consequences. These include the contamination of surface water through the release of leachate, the infiltration of leachate into lower layers, and the risk of highlighted contamination of underground water, which is causing poisoning and severe harm to the local population. Additionally, the feeding of indigenous animals with waste, the release of gas into the environment, and the detrimental effects on the existing plants and wildlife are worth mentioning. Hence, this study aims to provide a framework for assessing the economic and environmental elements of energy generation from municipal garbage in Gilan Province. Additionally, the study will analyze the feasibility of implementing this project from several economic and environmental perspectives. This study examines three methods, namely unsanitary depots (landfills), composting (organic fertilizer companies), and waste incineration facilities with gasification technology, in two scenarios to compare the effects of constructing or not constructing waste incineration power plants. The final model, incorporating GAMS software, is implemented using the exact number programming method.The findings are presented: The feasibility study for constructing a waste incineration plant utilizing gasification technology indicates a daily cost savings of $2,485,915. This plant would be capable of disposing of 600 tons of waste while also considering economic and environmental factors. Despite the fact that the construction of a power plant requires a relatively large initial investment, and it is apparently expensive, but in the end, with a sharp reduction in environmental consequences, as well as electricity production and side income generation in this way, the most optimal method among the available options in waste management is counted. https://sjie.journals.sharif.edu/article_24139.html This research deals with modeling and solving the maximal covering location problem, in which the facilities are composed of independent units that have the ability to move between facilities in different time periods and are called modules. The main goal is to provide maximum service to people who are in crisis during an accident and need basic services. Modules may become unavailable for any reason and fail to cover assigned demand. The problem model first determines the location of the facility and when a crisis occurs, according to the needs that arise in each demand area, it sends the modules responding to the same needs to the facilities in those areas. These modules are deployed in localized facilities and are moved to other demand areas after completion of service work. Therefore, the main objective is to allocate maximum basic and backup services to people who are in crisis. Another issue discussed in this article is that modules may become unavailable for any reason and fail to cover the demand assigned to them. For this reason, in addition to providing the main and basic service to the demand points, when the entire capacity of some modules is not available, the modules located in the nearby facilities can cover the demand of the neglected points and compensate for this deficiency by providing backup service.The mathematical model is a mixed integer problem, and a case study has been conducted on the third district of Tehran, where we have considered all the factors and parameters related to the model with special attention to the real conditions. The model has been solved by Gams software, and a solution based on Benders decomposition algorithm has been presented. Finally, sensitivity analysis was performed on some parameters of the model and it was observed that the changes in the parameters affect the backup service more than the basic service https://sjie.journals.sharif.edu/article_24140.html One of the challenges of creative industries can be considered as "quality management". What are the constituent elements of quality in creative industries, what are the characteristics of a high-quality creative product and how can the issue of quality be managed in these industries, among the challenges of creative industries activists is to benefit from new concepts such as Quality4.0, which arose from the concept "Industry 4.0" can help answer these challenges. One of the key elements of Quality4.0 can be considered "data-driven", which can help to solve the challenges of quality management in creative industries like a key, and by developing the concept of "data-driven quality management", provide a new perspective in quality management of creative industries. The upcoming research first forms a conceptual framework by formulating data-driven quality management in the special contingency framework of creative industries, which first describes and clarifies the dimensions of the concept of "quality" in creative industries; And then, based on that, he forms a research model in operations based on the "allocation problem" which provides a computational framework for quality management in creative industries with a computational look at the "quality" problem and in the process of achieving "quality quantification". It implements the model in the cinema industry as one of the important subcategories of creative industries. The model presented with the design and development of two innovative indicators called "quality rating" and "interaction and cooperation rate" for the production factors of a product in creative industries shows that it is possible to combine production factors and equipment with an a priori approach (before the start of creative product production). Optimized the use in the production of a creative product with the aim of maximizing the quality and minimizing the production cost, based on the needs of the owner of a creative product. https://sjie.journals.sharif.edu/article_24141.html The increasing growth of container transportation has led container terminals, as the primary hubs of cargo transfer in the global supply chain, to continuously improve their efficiency and operations to be successful in this competitive industry. Various problems have arisen in the maritime logistics field owing to the division of container terminals into two sections, quayside and yardside. This study examines the integration of problems on the quayside and the yardside. Specifically, it simultaneously investigates the quay crane scheduling problem as a quayside problem, along with the yard truck scheduling problem, yard crane scheduling problem, and storage space allocation problem as part of the yardside problems. A new integer linear programming model has been presented for the integrated problem of equipment handling scheduling and storage location allocation. This problem aims to minimize the time required to complete containers and operational costs. These operational costs include unloading containers from vessels using a quay crane, moving them to the yard via yard trucks, and loading them using a yard crane. The integrated problem is classified under the category of NP-hard problems in terms of computational complexity. Furthermore, they were introduced and added to the model according to the problem structure to expedite the resolution of constraints under valid inequalities. To validate the model's accuracy, instances were designed, implemented in GAMS software, and executed using the CPLEX solver. The computational results demonstrate that the incorporation of valid inequalities across all instances reduces computational time. Furthermore, in instances involving larger problems, where the mathematical model was unable to determine the optimal solution within a reasonable time, the addition of valid inequalities yielded solutions with an average gap of 1.78% from the lower bound. By contrast, the proposed model without the inclusion of valid inequalities achieved solutions with a gap of 3.08% from the lower bound. https://sjie.journals.sharif.edu/article_24142.html Logistics is one of the drivers of countries’ and companies’ aggressiveness and plays a vital role in economic growth. However, the current logistics industry is still high-cost and low-efficiency. The development of smart logistics brings opportunities to solve these problems. Despite many possibilities, the existence of some barriers limits the implementation of these systems. The purpose of this research is to evaluate the problem of the implementation of smart logistics. Failure mode and effect analysis (FMEA) method based on the fuzzy pivot pair-wise relative criteria importance assessment (F-PIPRECIA) and the weighted aggregated sum product assessment method based on Z number theory (Z-WASPAS) developed in three phases. In the first phase of this smart technology based on the literature, 16 smart logistics implementation barriers are identified using the FMEA method. In the second phase, using the fuzzy PIPRECIA method and experts' opinions, the weights of the factors are calculated. An adjusted PIPRECIA is proposed In the third phase and according to the outputs of the previous phases, the barriers are prioritized using the Z-WASPAS method. The FPIPRECIA-ZWASPAS approach produced to cover some disadvantages of FMEA method include inability to consider the different significance of risk factors in an uncertain environment. In addition to assigning different weights to factors and considering uncertainty, reliability is also considered in experts’ opinions through the theory of Z numbers. The proposed approach of this research was implemented in the evaluation of the barriers to smart logistics implementation in a factory active in the field of home appliances assembly located in the Aras Free Zone in Iran. Based on the results, This study has identified that “Lack of sufficient resources” has the highest priority among barriers. Also, “Lack of qualified staff” and “Administrative bureaucracy” are in the second and third priority, respectively. The results show the capability and efficiency of the proposed approach compared to other traditional methods such as FMEA and fuzzy WASPAS. https://sjie.journals.sharif.edu/article_24143.html Open-pit mining operations are complex and multifaceted, requiring meticulous planning to optimize resource extraction, operational efficiency, and cost-effectiveness. In this paper, a two-level hierarchical framework based on simulation is presented for production planning and material loading machines in Iran's largest open pit copper mine. Analytical-based simulation modeling offers a powerful tool for mining engineers and planners to simulate various scenarios, assess the impact of different decisions, and optimize mining operations. By integrating mathematical models, statistical analysis, and simulation techniques, this approach enables the evaluation of alternative strategies, risk assessment, and performance forecasting in open-pit mining environments. This paper aims to explore the application of analytical-based simulation modeling in the context of short-term and long-term planning in open-pit mining. It will delve into the methodology, tools, and techniques employed in developing simulation models for optimizing operational processes, enhancing productivity, and mitigating risks in open-pit mining operations. The study will also highlight the benefits, challenges, and future prospects of utilizing analytical-based simulation modeling for effective planning and decision-making in the dynamic field of open-pit mining. To solve the problem of planning machinery in mining, a planning problem has been formulated and applied in the framework of the proposed two-level hierarchical method. From the Opt Quest software package to solve the optimization problem at the high level of the proposed framework in order to prepare a long-term optimal production plan. At the lower level of the framework, with the help of a Meta modeling approach, a deterministic function has been fitted to accurately estimate the amount of loading machines by determining the optimal amount of shovels using the design of experiments. The calculations performed in this mining complex show how the proposed framework can have a favorable effect on the amount of production, material handling costs and income. By embracing analytical-based simulation modeling, mining managers can make informed decisions, mitigate risks, and drive operational excellence in open-pit mining environments. This approach empowers managers to proactively address challenges, capitalize on opportunities, and steer mining operations towards greater efficiency, profitability, and sustainability in both the short and long term. https://sjie.journals.sharif.edu/article_24144.html The topic of industrial location lately has gained significant global attention and importance in recent years. Evaluating the placement of heavy industries in a studied region involves considering criteria such as environmental factors, infrastructure, foundational impacts, and natural elements to incorporate. Methods like the Analytic Hierarchy Process (AHP) and VIKOR within a Geographic Information System (GIS) environment can be used for this evaluation. In the present study, an initial and preliminary assessment of the placement of heavy industries in the hinterland of Shahid Rajaee’s port have been conducted as a case study. By utilizing higher-level documents and studying the target region at each stage, including determining criteria based on the region, collecting data and information, and ultimately integrating this information and maps, we can be guided to the optimal location for industries placements. Therefore, the methodology will be practical and based on descriptive-analytical methods. Using the VIKOR method within the a Geographic Information System (GIS) software environment, the potential locations in the hinterland of Shahid Rajaee’s port have been divided into four hypothetical zones, numbered 1 to 4. The results of this research indicate that the most suitable areas for the establishment of heavy industries are those with access to main roads, flat terrain, prevailing wind direction, and distance from regional interferences as the determining criteria. The study highlights the importance of a systematic approach to industrial location, considering multiple factors to ensure the optimal placement of heavy industries. By integrating various data sources and using advanced analytical methods, the research provides a comprehensive framework for evaluating potential industrial sites. This approach not only helps in identifying the most suitable locations but also ensures that the environmental and infrastructural impacts are minimized, leading to sustainable industrial development in the region. Also, the results show that the eastern lands of hinterland have a special priority due to their proximity to the road and the existing facilities for the establishment of heavy industries, and in the next stage, the western lands should be allocated for light and administrative-welfare industries due to the access to the road from the west side. https://sjie.journals.sharif.edu/article_24153.html Ensuring a sustainable and reliable electricity supply on a large scale requires the optimization of energy resources and the efficient management of supply chains. Coal-fired thermal power plants play a vital role in electricity generation due to their high production capacity, relatively low fuel cost, and operational reliability. Nevertheless, these systems face significant challenges caused by the complexity of coal procurement, transportation, storage, and blending processes, in addition to stringent operational and environmental constraints. Selecting the optimal combination of various coal types with different qualities and costs is essential for minimizing transportation expenses, improving combustion efficiency, and maintaining stable power generation.Furthermore, production planning involves intricate scheduling decisions, including determining unit start-up and shut-down times, allocating generation loads, and meeting technical requirements such as minimum up and down times. When the coal supply chain and production scheduling are optimized separately, the resulting decisions are often uncoordinated, leading to inefficient resource utilization and higher total system costs.To overcome these limitations, this study develops an integrated Mixed-Integer Linear Programming (MILP) model designed to simultaneously optimize coal blending, storage, transportation, and production scheduling in coal-fired power plants. The model incorporates detailed operational characteristics of generation units, including start-up and shut-down costs, desulfurization costs, storage capacity limits, and production capabilities. The objective function aims to minimize total system costs, encompassing production, transportation, storage, fuel treatment, and shortage penalties.The proposed integrated optimization framework enables better coordination between the quantity and quality of coal supplied and the power generation requirements. The results demonstrate that this unified approach not only reduces total operational costs but also enhances fuel efficiency, lowers pollutant emissions, and improves the overall reliability and sustainability of electricity generation systems. This model can serve as an effective decision-support tool for planners and policymakers seeking to achieve efficient and environmentally responsible energy production. https://sjie.journals.sharif.edu/article_24159.html In the monitoring of manufacturing and service processes, it is sometimes necessary to measure the parameters of interest. One critical and often overlooked issue is the presence of measurement error. In this study, we propose the application of sampling methods based on Ranked Set Sampling (RSS) to mitigate the impact of measurement error. In this paper, the performance of the T2-Hotelling’s control chart for monitoring multivariate normal processes in the presence of measurement error is initially investigated using the classical additive model in Phase II. Subsequently, to mitigate the effect of measurement error in multivariate processes, novel sampling approaches based on Ranked Set Sampling (RSS) are proposed. The methods employed include standard RSS and Neoteric RSS (NRSS), which are extended to multivariate processes in this study, with ranking performed based on principal component scores.The performance of the proposed approaches is evaluated through simulation. The results indicate that the presence of measurement error significantly deteriorates the performance of the control chart due to a substantial increase in both the average run length (ARL) and the standard deviation of the run length (SDRL). Overall, the results demonstrate that the performance of the control chart using the proposed method based on standard RSS and NRSS is superior, due to lower ARL and SDRL values under out-of-control conditions, compared to the control chart performance in the presence of measurement error using simple random sampling. Furthermore, the performance of the proposed standard RSS and NRSS method improves with increasing sample size n as well as increasing the correlation coefficient between variables. Considering that the process is multivariate and there is correlation among the variables, this study proposes the use of principal component scores for ranking the correlated variables when applying the proposed RSS-based methods. The results also show that ranking based on the first principal component generally leads to better outcomes, as evidenced by lower ARL and SDRL values, compared to ranking based on the second principal component. Therefore, it is recommended that, in the proposed approaches, ranking be performed according to the first principal component. https://sjie.journals.sharif.edu/article_24210.html Optimal resource management in hospitals, particularly in critical departments such as operating rooms, plays a pivotal role in enhancing both economic performance and service quality. One of the key challenges in this context is the efficient scheduling of surgeries, considering resource constraints and uncertainty in surgery durations. This study focuses on developing a mathematical model for daily operating room scheduling under uncertainty whose decision variables include patient-to-operating room assignments, binary scheduling flags, start times per patient per scenario, staff assignments to operating rooms, sequencing relations between surgeries, operating room opening flags, overtime and under-utilization amounts, and bed allocations. The primary objectives of the model include improving collaboration satisfaction between surgeons and surgical assistants, allocating surgery times based on patient urgency, and reducing overall costs. The main innovation of the proposed model lies in the simultaneous consideration of staff collaboration preferences, comprehensive cost management, and the inclusion of personnel with varying specialties. Moreover, by incorporating different scenarios for surgery durations and influential factors such as surgeon skill levels, the model provides more realistic and applicable outcomes. To solve the problem under uncertainty, a novel genetic algorithm is presented. This algorithm features an innovative and comprehensive chromosome design and a customized crossover operator tailored to the specific characteristics of the problem. For instances where the optimal solution is known, the proposed algorithm achieves an average error margin of 1.26%. Additionally, for large-scale instances where commercial solvers fail to provide solutions within an hour, the performance and speed of the algorithm have been thoroughly evaluated. Computationally, the tailored chromosome design, repair mechanisms, and order-preserving crossover were critical to maintaining feasibility and producing high-quality sequences. Managerial metrics derived from GA outputs indicate reductions in operating room idle time and overtime exposure as well as fewer specialty-mismatch penalties and unscheduled patients compared to baseline schedules used in comparable literature instances. For practice, the proposed method offers hospital managers a flexible decision-support approach to produce realistic daily schedules under uncertain durations while balancing human factors and cost.