https://sjie.journals.sharif.edu/ Sharif Journal of Industrial Engineering & Management en daily 1 Fri, 20 Mar 2026 00:00:00 +0330 Fri, 20 Mar 2026 00:00:00 +0330 https://sjie.journals.sharif.edu/article_24273.html - 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. A 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) was 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 was proposed to cover some disadvantages of the FMEA method, including the 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_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 emissions. The environmentally sustainable healthcare facility location-allocation problem focusing on minimizing the carbon footprint 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 the 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_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_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 the 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 and silhouette diagrams. In the next step, by removing punctuation marks, stop words and words with fewer than three letters, converting the first letter of the words to lowercase and lemmatization, data cleaning was done. After that, topic modeling was done and each of the topics and words related to them were examined. In the next step, the topics were examined in different clusters.   These analyses 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 2041. 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_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 for creative industries activists is to benefit from new concepts such as Quality4, which arose from the concept "Industry 4", and can help answer these challenges. One of the key elements of Quality4 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). Optimizes the use of production factors 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_24139.html In this research, we look at a dynamic maximal covering location problem, where the structure of the service-providing facilities consists of independent units that have the ability to move, and these units are called modules. The main goal of the problem is to provide maximum service to people who are in crisis and need basic help. The model of this problem first determines the location of the facilities, and with the occurrence of each crisis, according to the needs that are created in each demand area, the modules responding to the same needs are sent to the facilities of those areas and are deployed in the location, from which they are moved away after the task of delivering service ends.  The innovation in this paper is that the modules may become unavailable for any reason and cannot cover the demand assigned to them. For this reason, modules, in addition to providing main services to demand points, in case of failure of some of these modules and their unavailability, the surrounding modules can cover the demand of points that have been neglected by providing backup service and compensate for this deficiency, where the amount of main and backup demand that each module responds to is determined by the model. The main goal of this research is to assign the maximum amount of main and backup service to people who are in crisis, which is of utmost importance to the livelihood of these people. In this research, we first created a mathematical model of mixed integer linear programming. Afterwards, we conducted a case study on Tehran's third district, where all the factors and parameters related to the model were considered with special attention to the real conditions of the studied area. After introducing the model and parameters related to district three, we solved the model using GAMS software. Furthermore, we implemented Bender's decomposition algorithm as an ultimate solution to the model.  Finally, we performed a sensitivity analysis on some parameters of the model. We observed that the changes in the parameters affect the backup service more than the base service.  https://sjie.journals.sharif.edu/article_24004.html This research addresses the challenge of optimizing energy dispatch in restructured power markets by evaluating a Virtual Power Plant (VPP) model that integrates demand-side storage. The study aims to close the gap between energy supply and demand by employing two optimization methodologies: a precise Mixed-Integer Linear Programming (MILP) model and a hybrid algorithm based on an Extreme Learning Machine and a Genetic Algorithm (ELM-GA), which is computationally efficient. The VPP model incorporates a diverse portfolio of generation units (diesel, hydroelectric, solar, wind), supply-side storage, and a prosumer, with demand-side management actualized through a water storage system. The objectives are to maximize VPP profitability while minimizing hydroelectric energy losses and pump operational costs. Tested on a standard IEEE 24-bus system with real-world data, the results show that a VPP with demand-side storage is 1.5 times less costly than a traditional grid without storage. Furthermore, the VPP framework is 1.7 times more cost-effective than a traditional grid structure. For large-scale problems, the ELM-GA algorithm delivers near-optimal solutions (4.4% optimality gap) 16 times faster than MILP, highlighting its potential for rapid, reliable energy management and policy-making. 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. 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 number of loading machines by determining the optimal number 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_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 groundwater 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 is apparently expensive, 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_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_24144.html The topic of industrial location has lately 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. 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 has 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 industry placement. Therefore, the methodology will be practical and based on descriptive-analytical methods. Using the VIKOR method within 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 the 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_24274.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_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_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. https://sjie.journals.sharif.edu/article_24253.html This study presents a multi-objective location-routing model for the management of electronic waste (e-waste) collection, which dynamically utilizes real-time data from the Internet of Things (IoT). Unlike previous studies that examined location, allocation, or routing decisions separately and within static environments, this research develops an integrated multi-level framework. In this framework, decisions regarding the optimal location of collection centers, the allocation of e-waste from generation points, and the routing of a heterogeneous fleet of vehicles are made simultaneously, based on real-time fill-level alerts received from IoT-equipped smart bins. For the first time in the literature, a dynamic service mechanism triggered by fill-level alarms is formulated as a bi-objective optimization model. The two objectives are minimizing total system costs—including fixed costs of collection centers, vehicle rental, and transportation—and minimizing greenhouse gas emissions generated during collection and transportation operations. This approach enables a shift from traditional fixed-schedule collection to a demand-responsive system, significantly improving operational efficiency. To validate the mathematical model, small-scale problem instances were solved using GAMS software and compared against exact solutions. For larger and more complex instances, an improved version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was developed and its performance was benchmarked against the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The results demonstrate that the enhanced NSGA-II outperforms MOPSO in terms of solution accuracy, proximity to the true Pareto front, and computational time. Furthermore, sensitivity analysis reveals that the capacities of collection centers and vehicles are the most influential parameters affecting both total cost and environmental emissions. A real-world case study conducted across 22 districts of Tehran confirms the practical applicability of the proposed model. The implementation of IoT-based fill-level alerts reduces unnecessary inspection trips by 32%, leading to 15.7% savings in fuel consumption and a 12.4% reduction in greenhouse gas emissions compared to conventional collection systems. These findings highlight the pivotal role of IoT in enabling dynamic and sustainable e-waste management. https://sjie.journals.sharif.edu/article_24271.html Pricing internet service plans is a complex and strategically important problem due to their multi-part tariff structure, which requires detailed modeling and careful analysis. These pricing schemes typically consist of two key components: a fixed fee that grants customers access to a predetermined data allowance, and a variable charge imposed on each unit of consumption beyond that allowance. Designing such tariffs becomes even more challenging when customer usage patterns exhibit uncertainty, as is common in real-world internet consumption. In this study, we investigate the pricing of internet service plans under a multi-part tariff structure while explicitly incorporating the stochastic nature of users’ monthly data consumption. Recognizing that customers differ not only in their usage levels but also in how they evaluate and select plans, we integrate several customer choice models into the pricing framework. These models capture different behavioral patterns and decision-making strategies, enabling the service provider to account for heterogeneity in customer responses. Building on these behavioral insights, we develop four mathematical optimization models that jointly determine both the optimal data allowance and the corresponding price for each plan under each choice model. These models allow for a systematic investigation of how behavioral assumptions and consumption uncertainty shape optimal pricing strategies. Our numerical results reveal that when usage is stochastic, understanding customer choice behavior has a substantial impact on the provider’s revenue. In particular, customer uncertainty about their actual consumption increases the likelihood of purchasing higher-volume plans or incurring additional charges, both of which contribute to higher revenue for the provider. Furthermore, the results show that expanding the menu of available plans does not necessarily improve profitability. In fact, under stochastic usage, offering too many plans may reduce revenue by enabling customers to more precisely match their expected consumption with the available tariff options. These findings underscore the importance of jointly considering usage uncertainty and customer choice behavior in the design of internet pricing schemes. https://sjie.journals.sharif.edu/article_24284.html This research paper presents a novel bi-objective mixed-integer nonlinear programming (MINLP) model designed to optimize truck scheduling operations within a cross-docking warehouse system. A key innovation of this study is its incorporation of the position-based learning effect of human resources, acknowledging that the time required to perform operations decreases with repetition due to increasing worker proficiency. This model simultaneously addresses two critical objectives: the minimization of makespan (the total completion time for all trucks) and the reduction of carbon emissions generated by warehouse activities. To accurately capture the environmental impact, an expanded objective function is introduced to calculate the carbon emissions resulting from the internal movement of goods, forklift operations, and truck idling times. Furthermore, recognizing the inherent uncertainties present in real-world logistics, such as variable processing and loading/unloading times, the model parameters are treated using triangular fuzzy numbers. This approach transforms the deterministic model into a robust bi-objective possibilistic programming framework, effectively handling epistemic uncertainty.This fuzzy model is then solved using an enhanced goal programming method, which efficiently navigates the trade-offs between the two conflicting objectives. The effectiveness and practical applicability of the proposed model and its solution methodology were rigorously validated through an extensive numerical analysis and an applied case study. The computational results demonstrate the model's significant potential, showing that integrating human learning effects can lead to a substantial 15.2% reduction in makespan. Concurrently, the strategic optimization guided by the emission-minimization function achieved a remarkable 75% decrease in carbon emissions from warehouse operations. This study underscores the profound benefits of integrating human factors and environmental sustainability considerations into logistics optimization. The proposed fuzzy-goal programming approach proves highly capable of managing real-world uncertainties while delivering dramatic improvements in both operational efficiency and ecological performance. These findings offer valuable, actionable insights for warehouse and logistics managers aiming to enhance productivity while minimizing their environmental impact. For future research directions, investigating dynamic learning models and exploring advanced multi-objective solution techniques for larger, more complex supply chain networks is recommended. https://sjie.journals.sharif.edu/article_24294.html The conceptual design of complex systems presents significant challenges due to their large scale, multidisciplinary nature, and dependence on technical, economic, and environmental factors. Such systems require advanced design methodologies capable of addressing multiple, often conflicting objectives. In response to these challenges, this research introduces a comprehensive framework for multi-objective modeling in the conceptual design of complex systems, with a focused application in the shipbuilding industry. The primary objective is to establish a systematic approach for achieving Design for Excellence (DfX) through the simultaneous optimization of diverse performance criteria. The proposed mathematical model is rooted in the DfX principles and incorporates key indicators such as cost, performance efficiency, system reliability, and technology and manufacturing maturity. A distinctive contribution of this study is the integration of DfX principles with the multi-objective evolutionary optimization algorithm NSGA‑III, enabling the generation of Pareto-optimal solutions that balance conflicting objectives while accounting for incompatibility and correlation constraints among available technologies. The model is also highly flexible, allowing it to be adapted to various types of vessels with different operational requirements. A case study in the shipbuilding industry demonstrates the practical effectiveness of the proposed framework. The obtained solutions exhibit a high degree of balance among the competing objectives. Quantitative comparisons show that NSGA-III outperforms MOEA/D and the Epsilon-Constraint method in three of the four objectives (OMOE, reliability, and risk). Relative to MOEA/D and the Epsilon-Constraint method, NSGA-III achieves improvements of 7.75% and 35.20% in Hypervolume, 17.35% and 40.88% in Spacing, 22.54% and 411.76% in the number of Pareto solutions, and 31.25% and 72.73% in Inverted Generational Distance (IGD), respectively. Although NSGA-III requires approximately 31.47% more computational time than MOEA/D, it is 44.12% faster than the Epsilon-Constraint approach.These results confirm the efficacy of the proposed model and establish it as a practical decision-support tool for engineering design of complex systems. https://sjie.journals.sharif.edu/article_24297.html The electronic payment industry, as one of the essential pillars of digital economy, operates in a dynamic environment characterized by economic fluctuations, rapid technological developments, operational disruptions, and growing service expectations. Under such conditions, selecting and continuously evaluating capable suppliers has become a strategic concern for payment service providers. This study aims to develop an integrated data-driven framework for evaluating agile and resilient suppliers within the context of digital transformation. First, the relevant supplier evaluation indicators were identified and organized into four main dimensions: agility, resilience, digital transformation, and general performance criteria. The fuzzy best–worst method (FBWM), based on the judgments of 18 experts, was then employed to determine the relative importance of indicators under uncertainty. The results indicated that fast delivery, source diversification, safety stock level, and crisis management experience were the most influential indicators, highlighting the critical roles of agility and resilience in the electronic payment industry. In the next stage, the weights obtained from FBWM were incorporated into the feature-scoring and selection process of the CatBoost algorithm. This mechanism allowed expert knowledge to guide the learning process while preserving the model’s ability to discover nonlinear patterns and interactions from actual supplier performance data. A case study was conducted in one of Iran’s leading electronic payment companies to examine the applicability and predictive performance of the proposed framework. The weighted CatBoost model achieved an MAE of 0.3468, an RMSE of 0.4462, and an R² value of 0.8673. The results demonstrated that the weighted model outperformed both the unweighted CatBoost model and the other machine-learning algorithms considered in the comparative analysis. Moreover, comparing the weighted and unweighted versions of the models confirmed that incorporating FBWM-derived weights provided an independent contribution to predictive improvement rather than merely reflecting the inherent performance of CatBoost. The proposed framework integrates expert-based prioritization with data-driven prediction and offers a reliable decision-support tool for supplier selection, ranking, and continuous monitoring. It can also assist managers in identifying strategically important supplier capabilities, allocating procurement resources more effectively, and improving supply continuity in technology-intensive and disruption-prone business environments.