Industrial management
laya olfat; seyed sourosh ghazinoori; maghsood amiri; TAHEREH MIRHOSEINI
Abstract
The limited natural resources of the Earth have presented various industries, including the dairy industry, with increasing challenges in the field of sustainable and efficient use of resources. The high consumption of water, energy, raw materials, as well as the significant production of waste and waste, ...
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The limited natural resources of the Earth have presented various industries, including the dairy industry, with increasing challenges in the field of sustainable and efficient use of resources. The high consumption of water, energy, raw materials, as well as the significant production of waste and waste, especially organic waste, in this industry have exposed it to environmental pressures. In order to improve the performance of organizations, new approaches for the optimal use of resources have been introduced, one of the most important of which is the concept of circular economy. In recent years, researchers have tried to combine this concept with other management concepts to create an effective framework for improving the performance of organizations. The present study attempts to identify and model the concepts of circular economy from the perspective of the EFQM 2020 Excellence Model in the dairy industry. For this purpose, first, the key indicators of the circular economy were identified using a systematic literature review and interviews with experts, and then these indicators were classified in the EFQM framework with the help of a panel of experts. Finally, confirmatory factor analysis was used to assess the accuracy of the designed model. The results obtained indicate the appropriate fit of the model and the meaningfulness of the relationships between the components. The findings of this study can be used as a model for establishing an excellence approach in the dairy industry based on a circular economy and play an effective role in formulating national and industrial policies.
Industrial management
Mohsen Keramatpanah; Mohammad Taghi Taghavifard; Maghsoud Amiri; Mehdi Mehdi Shamizanjani
Abstract
With the expansion of digital technologies and fundamental changes in the financial services ecosystem, the banking industry is facing new challenges on the path toward sustainable development. In this regard, the present study aims to propose a model for achieving sustainability in digital banking within ...
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With the expansion of digital technologies and fundamental changes in the financial services ecosystem, the banking industry is facing new challenges on the path toward sustainable development. In this regard, the present study aims to propose a model for achieving sustainability in digital banking within Iran’s private banking sector. To this end, key variables were initially identified through a review of the relevant literature and subsequently validated using the fuzzy Delphi method with input from subject matter experts. To analyze the relationships among the variables and determine their systemic structure, Interpretive Structural Modeling (ISM) was employed. Subsequently, a comprehensive digital banking sustainability model was developed and simulated using a system dynamics approach and related software tools. The results of the model analysis indicate that the development of technological infrastructure, enhancement of information security, improvement of customer experience, effective collaboration with regulatory bodies, and standardization of digital processes are key drivers in realizing sustainability in digital banking. Scenario analysis further reveals that simultaneous improvement of these components, by reinforcing positive feedback loops, can lead to sustainable growth and lasting competitive advantage for private banks. In this context, attention to sustainability requirements in digital banking and the development of related models can contribute to reducing operational costs in the banking network, optimizing energy consumption, increasing financial inclusion, and delivering more efficient services, thereby facilitating sustainable development. The primary contribution of this study is the development of an analytical and practical framework based on system dynamics modeling, which can support private banks in formulating strategies for sustainable digital transformation and serve as a roadmap for improving their economic, social, and environmental performance. The findings of this research can assist policymakers, banking executives, and researchers in the digital banking domain to better understand the factors influencing sustainability and to design effective interventions.IntroductionAs the banking industry experiences rapid digitalization driven by emerging technologies and evolving customer expectations, the necessity of aligning digital transformation with sustainability imperatives has become increasingly evident. This shift transcends traditional financial performance and calls for banking institutions to engage with social and environmental responsibilities while sustaining innovation. Particularly in developing economies such as Iran, the dual challenge of digital modernization and sustainable development presents a unique strategic frontier. In response to these dynamics, the present study proposes a comprehensive and dynamic model of sustainable digital banking, focusing on the private banking sector in Iran.Research BackgroundWhile digitalization and sustainability have been widely studied, there exists a significant gap in integrated models that account for the systemic interactions between the two domains. The prevailing literature tends to analyze digital banking from a technological perspective, often overlooking its environmental and social consequences (Del Carmen, 2022; Migliorelli & Dessertine, 2023). Similarly, sustainability-focused frameworks rarely include the disruptive potential of digital technologies such as blockchain, artificial intelligence (AI), and mobile banking. Some studies have suggested the relevance of digital sustainability in areas like waste reduction, financial inclusion, and eco-efficiency (Castro et al., 2021; Di Vaio et al., 2021), yet empirical applications remain sparse. By building on these foundations, this research aims to bridge conceptual and practical gaps by designing a policy-oriented, simulation-based model grounded in a system thinking approach.Materials and MethodsThis study adopted a hybrid methodology by integrating three complementary techniques—Fuzzy Delphi, Interpretive Structural Modeling (ISM), and System Dynamics (SD). Initially, a structured literature review spanning 2016 to 2023 was conducted using scholarly databases such as Scopus and Google Scholar. This led to the identification of 26 preliminary variables, which were refined and validated through expert consensus using the Fuzzy Delphi method. Experts were selected based on their experience in digital transformation initiatives and their roles in IT, innovation, or strategic planning within private banks. The Delphi rounds applied fuzzy logic to quantify agreement levels, ensuring rigor in variable selection. To structure the relationships among the validated variables, ISM was employed. This method facilitated the construction of a hierarchical framework to determine which variables serve as inputs, intermediaries, and outputs in the sustainability process. The final phase involved constructing a system dynamics model with feedback loops and flow diagrams to simulate the behavior of key sustainability indicators over time and under different scenarios. The sustainability model incorporated variables reflecting organizational, technological, social, and environmental dimensions. These include elements such as process improvement, trust, profitability, service quality, digital culture, smart leadership, electronic KYC, stakeholder satisfaction, employee welfare, and business model adaptability. By embedding these interconnected elements into the system structure, the model is designed to capture the complexity of digital sustainability transitions in a banking context.Data Analysis and FindingsThe system dynamics model as shown in Figure (1) was simulated across multiple scenarios to understand how different policy strategies would influence long-term outcomes. The simulation process ensured that model behavior aligned with historical data and logical expectations. Scenario analysis provided valuable insights into how banks can prioritize investments and strategic decisions to balance economic growth with sustainability. For instance, increasing investment in digital infrastructure and employee training led to noticeable gains in trust, customer acquisition, and organizational resilience. Similarly, enhancing authentication systems and E-KYC protocols improved operational efficiency while supporting environmental and social performance through reduced paperwork and increased accessibility. Findings confirmed that sustainability in digital banking is best achieved through a synergistic approach that combines internal cultural transformation with technological innovation and regulatory collaboration. Variables such as digital culture, smart leadership, and business model flexibility played a central role in facilitating systemic resilience. The results emphasize that merely focusing on digital upgrades is insufficient without aligning human capital and governance frameworks with sustainability objectives. Scenario comparisons further revealed that a balanced investment strategy targeting both operational excellence and environmental awareness yields the most robust outcomes.Figure 1:SD model ConclusionThe results of this research highlight the imperative for private banks in Iran and similar emerging markets to adopt integrated strategies that harmonize digital transformation with sustainability goals. This study makes a significant contribution by constructing and validating a system dynamics model that incorporates 26 interdependent variables derived from theory and practice, offering a holistic view of the sustainability landscape in digital banking. The model is unique in that it not only captures the technological enablers of sustainable banking but also embeds social and environmental dimensions, reflecting the true complexity of this transformation. The integration of Fuzzy Delphi and ISM methods with SD modeling allows for both structural clarity and dynamic simulation, enabling banks to test and adjust strategies before implementation. By embedding feedback loops and behavioral equations, the model delivers actionable insights into how various factors—such as trust, service quality, organizational agility, and stakeholder alignment—can collectively drive sustainability in the digital era. This comprehensive approach moves beyond linear planning, providing a real-time decision-making tool for managers, regulators, and researchers alike.Further Research IdeasFuture research can extend the current model by integrating real-time behavioral data through machine learning algorithms or agent-based modeling, offering more granular and predictive insights. Comparative studies across multiple banking systems or geographic regions would also enhance generalizability. In addition, introducing ESG indicators and green finance parameters could further enrich the environmental scope of the model. Finally, longitudinal studies using historical banking data can help validate the dynamic behavior of the system across different economic cycles.Managerial SuggestionsFrom a managerial perspective, several strategic recommendations emerge. First, banks should prioritize long-term investment in digital literacy and workforce development to foster a resilient culture aligned with sustainability values. Second, integrating advanced E-KYC and biometric verification tools can enhance customer trust and operational transparency. Third, collaborative governance involving fintechs, third parties, and regulatory bodies is essential for ecosystem resilience. Fourth, it is crucial to continuously monitor and update business models to reflect technological, societal, and environmental shifts. Finally, the adoption of sustainability-oriented performance metrics should be institutionalized to ensure that growth and digitalization are guided by ethical and ecological responsibility
supply chain management
Abolfazl Sadeghian; Seyed Mohammad Ali Khatami Firouzabadi; Laya Olfat; maghsoud Amiri
Abstract
Nowadays attending to closed-loop supply chain matter for survival in competitive circumstances not only has been become a controversial topic but also has been considered as a critical topic too. Close loop supply chain has combined to direct and reverse flow (method/ manner). This paper’s goal ...
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Nowadays attending to closed-loop supply chain matter for survival in competitive circumstances not only has been become a controversial topic but also has been considered as a critical topic too. Close loop supply chain has combined to direct and reverse flow (method/ manner). This paper’s goal is presenting a model for inventory control in closed loop supply chain by multiple objective approach. This research intends to reach it's main goal including reduce expenses such as production, maintenance, transportation in direct flow also decrease the waste material and defective in reverse flow and in conclusion increase the company’s profit by desingning and optimizing multiple objective model. Hence a double purpose model in closed-loop supply chain consists three classes direct flow in which conclude suppliers, manufactures and customers. Furthermore this consists four classes in reverse flow that concludes: collection centers, inspection, repair centers, recycling centers and disposal centers. According to the article’s model, which is multipurpose, linear and integer, At the beginning the model convert to single objective by Weighting and Constraint method and then is solved by using Branch and bound algorithm and Lingo software. Finally, the model extended in Iran Khodro Company as a study case and its function validated. Results and output of model solving demonstrate its capability to be useful for planning and inventory control in closed-loop supply chain.
Taher kouchaki tajani; Ali Mohtashami; maghsoud Amiri; Reza Ehtesham Rasi
Abstract
In this paper, we have proposed a model based on Mixed Integer Non-Linear Programming for the blood supply chain under conditions of uncertainty in supply and demand, from the stage of receiving blood from volunteers to the moment of distribution in demand centers. The challenges addressed in this optimization ...
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In this paper, we have proposed a model based on Mixed Integer Non-Linear Programming for the blood supply chain under conditions of uncertainty in supply and demand, from the stage of receiving blood from volunteers to the moment of distribution in demand centers. The challenges addressed in this optimization model are the reduction of blood supply chain costs along with minimizing the shortage and expiration rate of blood products. The Markov chain has been used to address the uncertainty of donor blood supply. To estimate the needs of medical centers, the received demand is considered fuzzy. Then, the proposed model is solved in small dimensions by GAMS software and in large dimensions by Bat and Whale meta-heuristic algorithms, and the results are presented. In addition, a case study is presented to show the applicability of the proposed model. The results show a reduction in the level of costs as well as a reduction in the shortage and expiration of blood products in the supply chain.IntroductionOne of the important topics researched in the global healthcare systems of different countries is the improvement of supply chain performance. The health system has one of the most complex and challenging supply chains due to its direct relationship with human lives. Issues such as uncertainty in blood demand and supply, blood inventory planning, delivery schedule, ordering time, attention to expiration date, and limited human resources are among the challenging issues in the field of health, especially the supply chain of blood and blood products. A unit of blood, from the time it is received from the donor to the time it is injected into the patient as whole blood or blood product, includes many processes and challenges that must be taken into account to ensure the health of the blood and the health of the supply chain. Redesigning an existing blood supply chain is not possible in the short term due to significant costs and time required, so using existing facilities and optimizing conditions is more preferable than reestablishing equipment, blood centers, and other facilities related to the blood supply chain. In this research, by presenting a mathematical model, we try to optimize the tools and facilities in a blood supply chain. The important goal in the blood supply chain is the cost factor. The costs incurred on the blood supply chain include costs such as blood collection from volunteers, product processing and blood inventory costs in hospitals and blood centers, and blood transfer costs to demand centers. On the other hand, the balance in storage and waste reduction is also very important in this chain. High storage increases the amount of inventory (increase in cost) and also increases the rate of perishability (increase in cost) of blood products. It is important to pay attention to the fact that the reduction of costs should be accompanied by the reduction of shortages and waste. In addition to the lack of blood, improper distribution and untimely supply of blood to hospitals can be completely disastrous. Requests to blood centers are made under certain conditions, such that the requested product(s) are separated in terms of blood group or the presence or absence of a specific antigen. Paying attention to blood groups and compatibility indicators is one of the principles of blood transfusion, and not observing them can cause unfortunate events.Due to the disproportionate percentage of distribution of blood groups among volunteers, there has always been a possibility of a shortage in the supply chain. In the medical world, in case of a shortage of a blood product of a certain group, attempts are made to replace that product from groups that can be matched. This will reduce the shortage and save the lives of patients whose blood with the required blood group and RH is not available at the same moment. In order to solve this challenge, in the upcoming research, a solution based on the versatility of unanswered demands will be considered, which will be included in the mathematical model. Another important issue is the age of the demand for the requested product, which creates an age-based demand in the supply chain. (Some special patients need fresh or normal products according to the type of disease.)MethodologyIn this research, a comprehensive mathematical model has been developed in the form of a MINLP model. The research model is based on a comprehensive blood supply chain consisting of three components: collection, processing, and consumption of blood products. There are three types of collection centers in this model: first, vehicles that serve blood donors at predetermined locations and collect blood; second, fixed collection facilities located in some areas of the city that solely perform the task of collecting blood; and third, blood centers (blood transfusion centers) that perform both blood collection work and other tasks related to product processing, testing, and transfer planning to demand centers and hospitals. The next part of the model is related to the processing of the collected blood. In this part, the blood collected by the collectors in the blood center is aggregated, the percentage of each blood group is determined, and according to the need in the blood centers, products such as red blood cells, platelets, and whole blood plasma are sent to hospitals. It is worth noting that as blood is converted into other products, some characteristics of the product, including the age of the products, differ from each other. Therefore, in the continuation of transferring the products and responding to their demand, the age of the blood product will be considered. Additionally, it should be noted that the blood product requested from the demand centers is in two forms. For some special patients and in special surgeries, a series of blood products with a certain age (young blood) are needed. Therefore, the importance of the age of the blood sent to the hospitals is also seen in the model. In the real world, in the face of a shortage in hospitals, a solution is thought out, which is to use the principle of adaptability of blood groups. Through a pre-accepted adaptability matrix, a series of demands for blood groups g, in case of shortage, can be satisfied with the supply of blood groups f turn around. Deterministic supply chain network design models do not take into account the uncertainties and information related to the future affecting the supply chain parameters and as a result cannot guarantee the future performance of the supply chain because due to the inherent and fluctuating and sometimes severe change in the environment of many operating systems Parameters in optimization problems have random and non-deterministic characteristics. In this research, two different approaches have been used to face the uncertainty in blood supply and demand values. For the demand, a triangular fuzzy approach has been proposed. According to the conditions of uncertainty, the appropriate alpha cut is selected based on the opinion of the decision-makers, and the demand is adapted to the conditions. Regarding the amount of supply, in order to estimate the number of donors in future periods, we have used the Markov chain to predict the number of donors based on the records in the past.FindingsIn order to evaluate the presented model, it is necessary to solve the research in both small and large sizes to determine the reaction of the research target function to changes in the parameters of the problem. For this purpose, the research model was first coded in GAMS 24.1 software. According to the designed sample problems, up to a certain size, it is possible to solve the problem within a certain time frame using GAMS software. However, as the size of the problem increases and the time to reach the answer also increases, meta-heuristic algorithms such as WOA and BAT were employed to solve this problem. The results indicate that the Whale Optimization Algorithm (WOA) performed better. Subsequently, based on a case study, a problem was presented to illustrate the efficiency of the model and its solution method. The results obtained for the objective function and the values obtained for the main variables of the research demonstrate the effectiveness of the model and its solution approach.ConclusionThe purpose of this article is to design a comprehensive supply chain that includes three parts: collection, processing, and distribution of blood products. The supply chain comprises mobile and fixed blood collection units that receive blood from donors and send it to blood centers. At these centers, blood is processed into required products and then distributed to demand centers based on demands categorized as fresh or normal products. In this research, the objective was to minimize costs such as blood collection, blood inventory in blood centers and hospitals, as well as the cost of blood products expiring due to non-use. To address blood deficiency, the blood compatibility system was incorporated into the model. This system ensures that if a certain product of a certain group is not available, a compatible product from another group is sent as a replacement. The model was solved using the exact solution approach of GAMS software for smaller-sized problems. However, for larger-sized problems, meta-heuristic algorithms such as WOA and BAT were employed to achieve reasonable solving times. Additionally, a fuzzy coefficient was proposed for relatively accurate demand prediction, and the Markov chain and the Kolmograph left-hand theorem were utilized to predict the number of blood donors. The results obtained from small-sized problems using accurate solver algorithms, as well as medium and large-sized problems using WOA and BAT meta-heuristic algorithms, demonstrate the efficiency of the designed model. Finally, a sensitivity analysis based on changes in fuzzy coefficients of demand and coefficients, including the alpha cut transformation function, and its effect on the objective function are presented.
maedeh mosayeb motlagh; Parham Azimi; maghsoud Amiri
Abstract
This paper investigates unreliable multi-product assembly lines with mixed (serial-parallel) layout model in which machines failures and repairing probabilities are considered. The aim of this study is to develop a multi-objective mathematical model consisting the maximization of the throughput rate ...
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This paper investigates unreliable multi-product assembly lines with mixed (serial-parallel) layout model in which machines failures and repairing probabilities are considered. The aim of this study is to develop a multi-objective mathematical model consisting the maximization of the throughput rate of the system and the minimization of the total cost of reducing mean processing times and the total buffer capacities with respect to the optimal values of the mean processing time of each product in each workstation and the buffer capacity between workstations. For this purpose, in order to configure the structure of the mathematical model, Simulation, Design of Experiments and Response Surface Methodology are used and to solve it, the meta-heuristic algorithms including Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Ranked Genetic Algorithm (NRGA) are implemented. The validity of the multi-objective mathematical model and the application of the proposed methodology for solving the model is examined on a case study. Finally, the performance of the algorithms used in this study is evaluated. The results show that the proposed multi-objective mathematical model is valid for optimizing unreliable production lines and has the ability to achieve optimal (near optimal) solutions in other similar problems with larger scale and more complexity.IntroductionA production line consists of a sequence of workstations, in each of which parts are processed by machines. In this setup, each workstation includes a number of similar or dissimilar parallel machines, and a buffer is placed between any two consecutive workstations. In production lines, the buffer capacity and processing time of machinery have a significant impact on the system's performance. The presence of buffers helps the system to maintain production despite possible conditions or accidents, such as machinery failure or changes in processing time. Previous research has investigated production lines without any possibility of machinery failure, referred to as "safe production lines." However, in real production lines, machinery failure is inevitable. Therefore, several studies have focused on "uncertain production lines,"assuming the existence of a probability of failure in a deterministic or exponential distribution. This research examines uncertain production lines with a combined layout, resulting from the combination of parallel deployment of machines within each workstation, if necessary, and serial deployment of workstations. The objective of this research is to determine the optimal values (or values close to optimal) of the average processing time of each product in each workstation, as well as the volume of buffers, as decision variables. The approach aims to maximize the system's output while minimizing the costs associated with reducing the processing time of workstations and minimizing the total volume of buffers between stations. Moreover, simulation can be applied without interrupting the production line or consuming significant resources. In this research, due to the high cost and time involved, implementing the proposed changes on the system is not cost-effective for investigating the changes in the production system's output rate. Therefore, the simulation technique has been utilized to optimize the production line.Research methodThe present study aims to develop a multi-objective mathematical model, based on simulation, to optimize multi-product production lines. In the first step, the structure of the multi-objective mathematical model is defined, along with the basic assumptions. To adopt a realistic approach in the model structure, the simulation technique has been employed to address the first objective function, which is maximizing the output rate of the production line. To achieve this, the desired production system is simulated. The design of experiments is used to generate scenarios for implementation in the simulated model, and the response surface methodology is utilized to analyze the relationship between the input variables (such as the average processing time of each product type in each workstation and the buffer volume between stations) and the response variable (production rate).ResultsTo implement the proposed methodology based on the designed multi-objective programming model, a case study of a three-product production line with 9 workstations and 8 buffers was conducted. Subsequently, to compare the performance of the optimization algorithms, five indicators were used: distance from the ideal solution, maximum dispersion, access rate, spacing, and time. For this purpose, 30 random problems, similar to the mathematical model of the case study, were generated and solved. Based on the results obtained, both algorithms exhibited similar performance in all indices, except for the maximum dispersion index.ConclusionsIn this article, the structure of a multi-objective mathematical model was sought in uncertain multi-product production lines with the combined arrangement of machines in series-parallel (parallel installation of machines in workstations if needed and installation of workstations in series). The objective was to determine the optimal values of the average processing time of each type of product in each workstation and the buffer volume of each station, with the goals of maximizing the production rate, minimizing the costs resulting from reducing the processing time, and the total volume of inter-station buffers simultaneously. To investigate the changes in the output rate of the production system, due to the high cost and time, it was deemed not cost-effective to implement the proposed changes on the system. Therefore, the combination of simulation techniques, design of experiments, and response surface methodology was used to fit the relevant metamodel. In the proposed approach of this research, taking a realistic view of production line modeling, the probability of machinery failure, as well as the possibility of repairability and return to the system, were considered in the form of statistical distribution functions. Additionally, all time parameters, including the arrival time between the parts, the start-up time of all the machines, the processing time, the time between two failures, and the repair time of the machines, were non-deterministic and subject to statistical distributions. Finally, to solve the structured mathematical model, two meta-heuristic algorithms (NSGA-II) and (NRGA) were considered.
maghsoud Amiri; mohsen shafiei nikabadi; Armin Jabbarzadeh
Abstract
In recent years, the complexity of the environment, the intense competition of organizations, the pressure of governments on producers to manage waste products, environmental pressures and most importantly, the benefits of recycling products have added to the importance of designing a closed loop supply ...
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In recent years, the complexity of the environment, the intense competition of organizations, the pressure of governments on producers to manage waste products, environmental pressures and most importantly, the benefits of recycling products have added to the importance of designing a closed loop supply chain network. Also, the existence of inherent uncertainties in the input parameters is another important factor that the lack of attention them can affect the strategic, tactical and operational decisions of organizations. Given these reasons, this research aims to design a multi-product and multi period closed loop supply chain network model in uncertainty conditions. To this aim, first a mixed-integer linear programming model is proposed to minimize supply chain costs. Then, for coping with hybrid uncertain parameters effectively, randomness and epistemic uncertainty, a novel robust stochastic-possibilistic programming (RSPP) approach is proposed. Furthermore, several varieties of RSPP models are developed and their differences, weaknesses, strengths and the most suitable conditions for being used are discussed. Finally, usefulness and applicability of the RSPP model are tested via the real case study in an edible oil industry.
pedram Pourkarim guilani; Mani Sharifi; parham azimi; maghsoud Amiri
Abstract
Due to the high sensitivity in applying of electronic and mechanical equipment, creating any conditions to increase the reliability of a system is always one of the important issues for system designers. Hence, making academic models much closer to the real word applications is very attractive. In the ...
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Due to the high sensitivity in applying of electronic and mechanical equipment, creating any conditions to increase the reliability of a system is always one of the important issues for system designers. Hence, making academic models much closer to the real word applications is very attractive. In the most studies in the reliability area, it is assumed that the failure rates of the system components are constant and have exponential distributions. This distribution and its attractive memory less property provide simple mathematical relationships in order to obtain the system reliability. But in real word problems, considering time-dependent failure rates is more realistic to model processes. It means that, the system components do not fail with a constant rate during the time horizon; but this failure rate changes over the time. One of the most useful statistical distributions in order to model the time-dependent failure rates is the Weibull distribution. This distribution is not a memory less one, so it was impossible to apply simple and explicit mathematical relationships as the same as exponential distributions for the reliability of a system. Therefore, researchers in this field have used simulation technique in these circumstances which is not an exact method to get near-optimum solutions. In this paper, for the first time, it is tried to obtain a mathematical equation to calculate the reliability function of a system with time-dependent components based on Weibull distribution. Also, in order to validate the proposed method, the results compared with exact solution that exists in literature.
