Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī

Data Envelopment Analysis (DEA) is an approach based on mathematical programming for the relative evaluation of decision-making units treated as similar yet distinct production systems. In this approach, the performance of each unit is characterized by describing the transformation of specific inputs into specific outputs. Traditional DEA models assume that the role of each performance factor is clearly defined. However, in some real-world problems, certain factors might be identified as dual-role factors depending on the evaluation nature or the decision-makers' perspective. These factors can play the role of both input and output, or even be considered neutral in assessing the units' performance. In the current paper, to determine the status of dual-role factors and calculate the efficiency of DMUs, two new linear programming models, based on the concept of deviation in the efficiency constraint and a common set of weights, are suggested. The main advantages of the proposed models are significantly reducing the computations and iterations required to solve the model, and involving all DMUs to determine the role of factors. To assess the performance of the proposed models, a data set for the evaluation of eighteen suppliers in the presence of two inputs, three outputs, and two dual-role factors has been employed. The obtained results showed that, compared to other models, the proposed models are computationally more efficient, and the role determination and evaluation of the units, based on the obtained weights from these models, are better aligned with the expectations of decision-makers

The evaluation and selection of suppliers is a crucial issue in supply chain management. This problem has grown increasingly uncertain due to the influence of imprecise parameters, and various tools have been proposed for weighting criteria and evaluating supplier scores for each criterion. Neutrosophic numbers, a modern tool for handling uncertainty, ambiguity, and inconsistency, are introduced to tackle such challenges. As the most comprehensive form of non-classical logic after fuzzy and intuitionistic fuzzy logic, Neutrosophic logic assigns degrees of membership, non-membership, and hesitation independently between zero and one. In this research, the quality function deployment (QFD) technique is developed in a Neutrosophic environment to identify and weigh supplier evaluation criteria. Additionally, the Neutrosophic EDAS approach is proposed for selecting top suppliers. A numerical study of the pharmaceutical industry demonstrates that geographical location and supplier experience are the two most critical criteria, and the second supplier is chosen as the best.

Scientific and technological developments, especially in the field of information and communication technology, have caused significant changes in people's daily lives, and remote work has been promoted as a new approach in managing work and performing activities, especially in the conditions created by the Corona pandemic. This study examines the concept of remote work in a special framework that includes the Corona period and the fifth industrial revolution and emphasizes the need for comprehensive scientific attention and innovative methods. To solve these challenges, this research presents a comprehensive model by applying the soft systems methodology (SSM), which takes into account the perspectives and limitations of different stakeholders and aims to increase the implementation of remote work. Using systematic methods and qualitative data analysis, this study develops the model in a flexible and comprehensive way. In addition, it highlights the role of technology, organizational culture and management strategies in reducing social isolation and increasing telework efficiency. The findings emphasize the dynamic aspect of the remote work ecosystem and emphasize the importance of multifaceted solutions for organizational success in the era of the fifth industrial revolution.

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.

In this era of rapid changes, industries must be flexible and responsive to survive in the market. Achieving this flexibility and responsiveness requires learning from the environment, alongside fostering innovation, creativity, and possibly launching new products. Different learning strategies lead to varying outcomes; thus, this research explores the effects of learning strategies on innovation, creativity, and the performance of new products in small firms.The experience of the 2008 economic crisis in Europe demonstrated that firms' intangible assets, such as employee skills, experience, and knowledge, played a critical role in preventing bankruptcy. This realization led companies to recognize assets such as personnel abilities in learning, creativity, innovation, and the integration of practical knowledge as key sources of competitive advantage. Research has shown that small firms, in addition to their positive impact on economic growth, outperform larger firms in terms of adaptability and creativity. The service portal of the Organization for Iran Small Industries and Industrial Towns reported over 50,000 active small firms by the end of June 2022, underscoring their significance in the country's economy. However, the high failure rate of new products and the limited resources available to small firms make the success of new product development a critical issue.

Occupational Health and Safety (OHS) management significantly affects reducing costs, increasing productivity, and the social credibility of construction companies and plays a facilitating role in the transition towards sustainable development. This study aims to identify and quantitatively analyze OHS risks in sustainable construction projects in Iran. To do this, first, common OHS risks are identified by conducting library studies. Then, these risks are screened and localized for Iran's sustainable construction projects by surveying 13 experts, selected by the snowball sampling method, in a focus group meeting. Afterward, each risk's importance and priority are determined using the Neutrosophic Group Best-Worst Multi-criteria (NGBWM) method, while applying a weight to each expert's opinion. The Neutrosophic sets theory provides the basis for obtaining accurate and more reliable results by considering the uncertainties in the experts' opinions. The findings showed that "the lack of sufficient safety skills of employees due to not allocating time to specialized safety training," "occupational injuries and diseases," "hazards caused by improper design and layout of the project site," "Weakness and inefficiency of occupational health and safety management personnel," and "Negligence and lack of planning for emergency maneuvers," with weights of 0.052, 0.036, 0.035, 0.032, and 0.028 respectively, are the most critical OHS risks in Iran's sustainable construction projects. Finally, reactive and preventive responses were proposed to face them in detail.IntroductionThe construction industry is one of the most dangerous industries worldwide, and Iran is no exception. According to reports from Iran's official institutions, 30-35% of work-related accidents occur in the construction sector. Furthermore, analysis of construction accidents indicates that 22% of accidents occur in the stages of preparation and demolition, while 61% occur during the construction phase (Alipour-Bashary et al., 2021). Research has revealed that sustainable construction projects pose a greater risk to health and safety than conventional construction processes. The health and safety of workers are essential aspects of social sustainability. However, the importance of health and safety risk assessment in sustainable construction projects is still in its early stages (Onubi et al., 2019). Given the complexity and challenges in the Occupational Health and Safety (OHS) risk assessment environment, it is crucial to develop a suitable mechanism for identifying and measuring safety risks in sustainable construction projects. This would enable finding the best solutions for risks that have a high probability of occurrence and severe consequences. The current research aims to answer the following main question: What are the key OHS risks in sustainable construction projects in Iran and the appropriate response and preventive actions for them?Literature eviewReviewing previous research shows that while risk management in construction projects is not a new concept, the focus on the safety of construction projects in recent years is a relatively recent development. Furthermore, with leading international companies in the construction industry increasingly embracing sustainable development, there is a growing interest in integrating safety risk management with sustainable practices, making this perspective unique and novel. Previous studies on the safety risks of construction projects have typically categorized these risks within the dimensions of Health, Safety, and Environment (HSE), often neglecting other dimensions of safety risks. In contrast, the current research proposes to combine the three aspects of sustainable development (economic, social, and environmental) with the dimensions of HSE, thereby offering a more comprehensive framework for organizing the safety risks of construction projects. A significant research gap in this field lies in the evaluation and quantitative analysis of identified risks. To address this gap, the current research employs the Neutrosophic Group Best-Worst Multi-criteria (NGBWM) method, which involves weighting experts' perspectives to provide a more robust and reliable assessment of safety risks.MethodologyThe current research was applied with a purposeful and descriptive survey approach. Data were collected from 13 project managers and executive officials in Iran's sustainable construction projects, sampled using the snowball method. Semi-structured interviews and two researcher-made questionnaires were employed to gather the required data. The research objectives were pursued through a proposed methodological framework comprising five main phases. In this study, Occupational Health and Safety (OHS) risks in sustainable construction projects were evaluated and analyzed within a neutrosophic space and through group decision-making. Following the identification of the final risks, the Neutrosophic Group Best-Worst Multi-criteria (NGBWM) method was applied using the General Algebraic Modeling System (GAMS) to measure importance and determine high-ranked risks. The group decision-making approach aimed to mitigate bias in results and enhance decision accuracy by leveraging collective wisdom. Implementing the NGBWM method in the neutrosophic space helped reduce uncertainty in subjective judgments and enhance decision accuracy through the use of three or four-point estimates and consideration of possibility functions for experts' opinions. ResultsAccording to the results, 45 Occupational Health and Safety (OHS) risks were identified for Iran’s sustainable construction industry. The application of the Neutrosophic Group Best-Worst Multi-criteria (NGBWM) method revealed that risks such as "lack of sufficient safety skills of employees due to not allocating time to specialized safety training," "occupational injuries and diseases," "hazards caused by improper design and layout of the project site," "Weakness and inefficiency of OHS management personnel," and "Negligence and lack of planning for emergency maneuvers," respectively, had the most significant importance and the highest ranks, with weights of 0.052, 0.036, 0.035, 0.032, and 0.028.DiscussionThe inadequacy of specialized training programs in the field of safety has been identified as the root cause of many OHS risks in Iran’s sustainable construction projects. The role of the human resources unit in enhancing and nurturing a skilled and knowledgeable workforce in the principles and standards of safety in sustainable construction projects is more crucial than ever. It is imperative to prioritize the quality of work and personal life of human resources. Designing an efficient incentive system, providing health insurance for employees, conducting periodic check-ups, and offering comprehensive training programs can serve as preventive measures to mitigate the occurrence of injuries and occupational diseases. These proactive steps not only enhance workplace safety but also contribute to the overall well-being and productivity of the workforce in sustainable construction projects.ConclusionThe occurrence of safety risks in sustainable construction projects is multifaceted and does not solely stem from individual carelessness or unexpected accidents. Instead, these risks originate from various areas including social, managerial, structural, and health domains. Consequently, solely focusing on OHS risks in a one-dimensional manner and lacking a systematic and comprehensive view of this issue hinder managers and decision-makers from accurately understanding and analyzing the main sources of risks and implementing appropriate preventive measures.

In combined cycle power plants, instead of releasing gases produced from burning fossil fuels, after turning the gas turbines, they enter into heat recovery steam generator (HRSG) boilers to produce steam. The produced steam by these boilers is used to generate electricity in steam turbines and thus, electricity generation efficiency is dramatically increased. In this way, the efficiency of electricity production increases significantly. These boilers are made at a great cost and also, any failures of them cause a power plant to stop and create enormous costs, so optimizing their reliability is very important. This paper deals with the modeling of the HRSG feed water system by using a block diagram for two states (i.e., half-time and full load), to evaluate the difference between the proposed alternative designs, by considering their reliability. The method used in this paper can be applied to evaluate and optimize the reliability of many other industrial systems. Introduction In power generation, the reliability of industrial control systems is crucial, as failures can disrupt services, leading to accidents and damages. This study focuses on the reliability of Heat Recovery Steam Generator (HRSG) boilers in combined cycle power plants. These plants optimize electricity generation by redirecting gases from burning fossil fuels into heat recovery steam generators. HRSG boiler reliability is pivotal due to high construction costs and the potential for extensive downtime and expenses in case of malfunctions. Addressing this challenge, the research employs the underutilized Reliability Block Diagram (RBD) model, providing a graphical representation of system components and interactions. Specifically tailored to the needs of the Mapna Boiler Company, the study aims to assess and optimize the reliability of the steam production unit, i.e., the boiler, within combined cycle power plants. Research BackgroundReliability, in conjunction with factors such as availability and safety, stands as a cornerstone in ensuring the practical quality of any system. The application of Reliability Block Diagrams (RBD) is a well-established method for modeling and calculating the reliability of industrial systems. Numerous studies have applied RBDs across diverse domains, ranging from power substation automation and wind turbine reliability to error calculations in intelligent submarine power systems. However, despite the versatility of RBDs, a noticeable gap exists in the literature regarding their use for modeling boiler reliability, especially as a multi-state system. Research MethodologyTo undertake a comprehensive reliability analysis of HRSG boilers, the study focuses on distinct subsystems, including:Feed-Water Storage SystemFeed-Water System (FWS)High-Pressure (HP) SectionLow-Pressure (LP) SectionCondensate SystemChemical Dosing System.The Feed-Water System (FWS) is crucial for immediate boiler operation. The initial design involves a Four-Pump System (A2 design) for the FWS. A modification is proposed, removing one feed-water pump, prompting an examination of its impact on boiler reliability. Critical components are identified based on their role in potential disruptions, emphasizing parts causing immediate boiler shutdowns. Using expert knowledge and diagrams, a Reliability Block Diagram (RBD) is developed, visually highlighting weak points. The RBD assesses FWS reliability, comparing two configurations for optimization. Calculation of HRSG Boiler Reliability as a Multistate SystemConfigurations of three-pump and four-pump setups for the Feed-Water System (FWS) are illustrated and analyzed using the Reliability Block Diagram (RBD). The reliability analysis entails a detailed process of data gathering, failure rate determination, and overall reliability calculation for diverse system configurations. The study incorporates probabilities for various operational states and introduces mathematical formulations to calculate the Mean Time Between Failures (MTBF) for water feed system configurations.Fig1: Configuration of HRSG boiler water supply system in 3 pump modeFig2: Configuration of HRSG boiler water supply system in 4 pump mode Steps of optimizing the operational reliability (OPR)Step 1: Identifying Components Used in FWSStep 2: Determining Failure Rates for Each ComponentStep 3: Drawing a Reliability Block Diagram (RBD)Step 4: Evaluating Component ReliabilityStep 5: Calculating Overall Reliability for Each Configuration. ResultsTables present Mean Time Between Failures (MTBF) for water feed system configurations, offering insights into the trade-offs between complete shutdowns and demi-capacity operations. The analysis suggests that the four-pump configuration, while experiencing fewer complete shutdowns, operates at half capacity more frequently compared to the three-pump configuration. The data-driven results highlight the nuances of system reliability and its dynamic nature. Research FindingsThe reliability assessment for boiler construction, considering the failure rates of components over a one-year period, indicates that the four-pump configuration is superior when component reliability is high; otherwise, the three-pump configuration may have an advantage. However, the decision to choose between these configurations necessitates an economic evaluation, accounting for construction costs, shutdown expenses, and half-capacity operation costs. The study underscores the importance of integrating economic considerations with reliability assessments for informed decision-making. Discussion and ConclusionThis research offers valuable insights into vulnerable areas of the HRSG boiler water feeding system, guiding maintenance attention and informing decision-making processes. The study emphasizes the need for future research to consider repair times and incorporate fuzzy reliability values to enhance the robustness of reliability calculations. The holistic approach adopted in this study, combining technical assessments with economic considerations, lays the groundwork for a more comprehensive understanding of system reliability in industrial settings. Suggestions for Future ResearchAs industries evolve, future research should tailor reliability models to specific contexts. Exploring different failure distribution functions beyond the constant-rate assumption opens avenues for investigation. Models like the Weibull mixture model, competitive risk models, compound models, and hybrid models offer promising directions. For instance, the study proposes exploring the application of a compound renewal model, known as complementary risk, for systems with parallel performance and independent components. The limited exploration of this model in the literature presents an opportunity for future research to uncover its potential applications and contributions to reliability modeling.

Maintenance is a significant cost factor in supply chains and production systems. Intelligent maintenance systems are gaining global interest with the rise of IT-based technologies. This study focuses on a new reliability-centered maintenance (RCM) method, where an intelligent system decides on repair or replacement based on system reliability in the practical flexible job shop scheduling problem (FJSP). The research introduces a balance by integrating industrial units' green production and energy consumption with traditional objectives like production costs and system reliability in a multi-objective framework. Another key aspect is the consideration of less developed regions as a social factor in the energy calculation model, resembling the Ministry of Energy's computational models. The reliability model is tailored for a complex system with multiple machines having time-dependent lifespans and repair probabilities, where operation times, maintenance times, and post-repair reliability levels are all stochastic. Metaheuristic algorithms combined with simulation-based optimization are used to solve the model. Statistical and non-statistical methods are used to depict the performance of the algorithms. The study shows that these algorithms effectively solve complex multi-objective stochastic problems and can be considered as a decision support system (DSS) for software developers working on real-world applications.

The supply chain of the food industry is crucial for countries, yet it is vulnerable to disruptions caused by natural disasters like floods, frost, and heatwaves, as well as operational shutdowns. These disruptions can trigger a ripple effect throughout the food supply chain, posing significant challenges for the country. Therefore, it is imperative to identify and analyze strategies to mitigate the ripple effect. This research has been conducted in two stages: qualitative and quantitative. The qualitative stage aimed to identify coping strategies, employing thematic analysis. The quantitative stage involved scenario modeling and analysis using fuzzy cognitive maps. The findings revealed 84 primary codes grouped into 21 sub-categories and 4 main categories: "Strategic Management," "Operations Management," "Compilation and Correct Implementation of Laws," and "Supply Chain Management." Analysis of backward scenarios underscored the importance of "supplier relationship management," "cooperation and coordination in the supply chain," and "contingency plans." Conversely, analysis of forward scenarios highlighted the significance of "monitoring environmental changes" and "strategic planning." Focusing on short-term plans, enhancing managers' decision-making and problem-solving skills, refining supplier selection criteria, optimizing supply network design with backup locations, and maintaining safety stock for critical goods are recommended actions for industry stakeholders.IntroductionThe growth of supply chains and their increasing interdependence raise concerns about vulnerability and the likelihood of supply chain failure (Kek et al., 2022). One significant contributor to supply chain failure is the propagation of disruption, commonly known as the ripple effect (Ghadge et al., 2022). The ripple effect exerts various negative impacts on the agricultural supply chain (Wei & Chen, 2010), with factors such as climate change exacerbating these effects on the agricultural sector and food supply chain (Galli et al., 2023). A prominent example of the ripple effect is the COVID-19 pandemic, which led to crises in the food supply chain, including human resource shortages, transportation disruptions, and input cost escalations (Waris et al., 2022). In Iran, the pandemic significantly disrupted the food supply chain, resulting in decreased profitability, sales rates, flexibility, and investment returns (Afzali and Zare Mehrjardi, 2020). Thus, investigating this issue in Iran's food supply is imperative. The objectives of the research are:Identifying strategies to cope with the ripple effect in Iran's food product supply chain.Presenting a fuzzy cognitive map of strategies to cope with the ripple effect in Iran's food product supply chain.Conducting scenario analysis of strategies to cope with the ripple effect in Iran's food product supply chain.Materials and MethodsThis research adopts a mixed-method approach, comprising qualitative and quantitative stages. In the qualitative stage, participants include experts and managers with a minimum of 10 years of experience in the food processing supply chain, possessing academic qualifications, and experience with supply chain disruptions. The statistical population for the quantitative stage encompasses the participants from the qualitative stage, supplemented by university professors with publications in the field of supply chain ripple effects. Thematic analysis is employed in the qualitative part to analyze the data. Subsequently, based on the qualitative findings, a researcher-designed questionnaire is developed for the quantitative phase. The fuzzy cognitive map method is then utilized to analyze the quantitative data gathered.ResultsSemi-structured interviews were conducted with experts to identify strategies for coping with the ripple effect in Iran's food supply chain. From these interviews, 84 primary codes were identified, which were then organized into 21 sub-categories and 4 main categories: "strategic management," "operations management," "drafting and correct implementation of laws," and "supply chain management." Notably, nearly half of the obtained codes were attributed to the "supply chain management" category, indicating its significant importance in addressing the ripple effect. In the second stage of the research, a questionnaire was designed based on the findings of the previous stage and administered to 10 experts for completion. In this questionnaire, experts were asked to assess the importance of each of the 21 sub-categories. Subsequently, FCMapper software was employed to construct a fuzzy cognitive map depicting coping strategies.Table 1: Analysis of strategies to cope with the ripple effectTypeCentralityOutdegreeIndegreeStrategyTotal Componentsordinary17٫295٫7311٫56121ordinary12٫32٫459٫852Total Connectionsdriver10٫1110٫1103191ordinary11٫128٫972٫154Densityreceiver9٫6409٫6450.45ordinary8٫282٫985٫36Connections per Componentordinary16٫914٫8712٫0479.09ordinary10٫278٫911٫368Number of Driver Componentsordinary17٫646٫9110٫7393ordinary10٫586٫434٫1510Number of Receiver Componentsordinary5٫192٫552٫64111driver5٫815٫81012Number of Ordinary Componentsdriver8٫98٫901317ordinary16٫336٫331014Complexity Scoreordinary16٫397٫379٫02150.33ordinary8٫897٫641٫2516ordinary15٫816٫369٫4517ordinary14٫184٫849٫3418ordinary11٫644٫197٫4519ordinary4٫723٫261٫4620ordinary11٫487٫134٫3521As shown in Table 1, 'Environmental change monitoring,' 'Strategic planning,' and 'Technology upgrade' strategies have the highest degree of effectiveness, while 'Inventory management,' 'Contingency programs,' and 'Production flexibility' strategies also exhibit high effectiveness. Furthermore, 'Production flexibility,' 'Contingency plans,' and 'Inventory management' demonstrate the highest degree of centrality. Figure 1 depicts the fuzzy cognitive mapping of strategies to cope with the ripple effect in the supply chain of Iran's food products.Figure 1: Fuzzy cognitive mapping of strategies to cope with the ripple effect To examine the scenarios, three backward and three forward scenarios were designed. In the backward scenario, the most effective variables were selected. Figure 2: The first backward scenario of coping strategiesCooperation and CoordinationSupplier Relationship ManagementContingency PlanningInventory ManagementFigure 3: Second backward scenario of coping strategiesSupplier Relationship ManagementCooperation and CoordinationContingency PlanningFigure 4: The third scenario backward coping strategiesCooperation and CoordinationSupplier Relationship ManagementContingency PlanningProduction FlexibilityFigure 5: Overlap of the backward scenarios of coping strategiesCooperation and Coordination Supplier Relationship Management Production Flexibility Contingency Planning Inventory Management To draw forward scenarios, strategies No. 3, 4, and 8, which represent 'monitoring environmental changes,' 'strategic program,' and 'technology improvement,' respectively, were selected.Figure 6: First forward scenario of coping strategiesMulti-Skilled WorkforceShort Term PlanningHRMTechnology UpgradeMonitoring Environmental Changes Figure 7: Second forward scenario of coping strategiesHRMMulti-skilled WorkforceShort Term Planning Horizontal IntegrationStrategic Planning Figure 8: The third forward scenario of coping strategiesMulti-Skilled Workforce Short Term PlanningHRMTechnology Upgrade Figure 9: Overlap of the forward scenario of coping strategiesMulti-skilled Workforce Short Term Planning HRMTechnology Upgrade Monitoring Environmental changes Horizontal IntegrationStrategic PlanningConclusionsFood product supply chain managers should consider long-term factors, price flexibility, and contract support clauses in contracts with suppliers. For foreign products, it is recommended to contract with companies that have active agencies in the country, as other companies may quickly cease their services due to new sanctions. The purchase of critical parts of the supply chain, known as vertical integration, is recommended to reduce risk. Contingency plans are necessary to cope with the ripple effect, but to develop suitable contingency plans, environmental and political issues must be carefully monitored. As a result, it is necessary to create management teams in food products to investigate environmental issues.

The correct storage and arrangement of products in the warehouse increase efficiency in responding to requests, accelerate the identification of products, increase accessibility of items in the warehouse, make more use of available space in the warehouse, reduce the possibility of product damage, and increase flexibility. The review of studies in the field of warehousing and arrangement of products in warehouses revealed that the use of machine learning algorithms in this field is one of the important research gaps. Therefore, in this article, using machine learning algorithms, we aim to present an innovative algorithm for allocating goods to different parts of a warehouse, for which a real case study is used. The goal of categorizing products based on their characteristics is to minimize the total cost of the system. Hence, spatial clustering algorithms based on the density of applications with noise (DBSCAN), self-organizing mapping neural network (SOM), and AGNES are used. The obtained results show that SOM has better performance than DBSCAN. Also, the DBSCAN algorithm performs better than AGNES.IntroductionWarehouses play a crucial role in every supply chain that involves activities such as receiving, storing, picking, and transporting goods. The way goods are stored directly affects the costs associated with warehousing, so it is important to have efficient management systems in place in order to stay competitive in the global market. Having an organized warehouse layout, utilizing technology for inventory management, and implementing streamlined processes can all contribute to reducing costs and increasing efficiency in warehousing operations. By continuously optimizing operations and staying up-to-date with industry trends, businesses can ensure they are meeting customer demands and staying ahead of the competition (Jinxiang Gu et al., 2007). Storage is the primary and essential function in all warehouses. The methods used for storing items can vary depending on the type of warehouse and its specific goals and objectives (Berman, 1996). The main goal of storage and warehouses is to meet the needs of consumers or enhance service in a manner that takes into account limitations in resources. Efficient management of storage also helps to enhance the speed and reliability of deliveries, which has been identified as a crucial factor for performance in the last twenty years (Ann E. Gary et al., 1992). When looking at logistics costs from an economic perspective, the costs associated with storage and warehousing services make up around 15% of the total logistics costs in developed countries like Germany (Handfield et al., 2013). In this context, properly allocating storage can reduce costs. After deciding how to store the goods, we determine their arrangement. The purpose of this article is to determine the optimal arrangement of goods in the dedicated storage system. Arranging the goods logically in the warehouse increases efficiency in responding to requests, accelerates goods identification, increases accessibility, makes better use of space, determines the location of goods and protects them. It also provides more flexibility and more suitable conditions for storage. It should be noted that due to the functional nature of warehouses, which requires rapid response to determine optimal goods placement, innovative solutions are imperative. All algorithms proposed to solve organizing goods in warehouses must completely consider inclusiveness according to attributes like grouping, similarity, flammability, degradability, inbound/outbound amounts, and stockroom area. Therefore, according to the huge volume and diversity of data in these systems, utilizing data extraction strategies can maximize efficiency of mathematical planning models whose inputs include inbound/outbound amounts for each good and stockroom area assigned. This confirms arrangements account for qualities like item classes, quantities, traits, and warehouse restrictions. Usually, algorithms presented by these methods typically have some limitations. For example, you could reference the inventory of products stocked in your warehouse. A useful way to enhance or address existing issues is through the use of data-driven and machine learning techniques. In this work, we aim to improve an innovative algorithm described in prior studies using data-focused and collaborative learning approaches. Next, we will provide a brief overview of the framework. Then, the problem definition and mathematical model are described. Following, the methods and analyses employed and findings obtained are examined. After, the effect of the algorithm on performance metrics is assessed. Later, applications of machine learning methods for inventory are explained. Finally, results and recommendations are presented.MethodAccording to the items found in the storage facility, nine characteristics for goods were identified, such as group one, group two, similarity, combustion, combustible, corruption, violation, the quantity of goods entering and leaving the warehouse, and storage space extracted. Subsequently, 17 warehouse performance indicators were used to calculate the cost function through a mathematical programming model, analyzing 55 different scenarios. The commodities were then classified using machine learning algorithms SOM, DBSCAN, and AGNES, based on the identified characteristics and inventory performance indicators, with the cost function calculated for each algorithm. Finally, a comparison was conducted between inventory performance indicators and the cost function using the mathematical planning model and the suggested algorithm, with performance evaluated through statistical tests like the Levene test, Kruskal Wallis test, and the Brown for Syte test.ResultsBased on the inventory of 2800 different types of products in the warehouse of Farasan Industrial and Manufacturing Plant, characteristics were extracted for each product. Additionally, warehouse performance indicators and cost functions were analyzed using mathematical programming models and machine learning algorithms. The performance of three algorithms was compared with a mathematical algorithm through statistical tests such as Levene's test, Kruskal-Wallis test, and Brown-Forsythe test. The results showed that the SOM neural network was more efficient than the other two algorithms. Thus, by combining mathematical programming models and machine learning algorithms, one can improve warehouse performance and reduce costs, providing optimal solutions for factory inventory management.ConclusionIn previous research, it was found that products were stored in warehouses without any prior processing. This created a gap in the field, highlighting the importance of categorizing similar goods before storing them in warehouses to reduce storage costs for factories and manufacturing companies. To address this issue, a sophisticated algorithm was developed to enhance product quality in warehouses across all industries. Reducing storage costs is a common objective for companies and factories, influenced by various factors in their environments. This research focused on developing a model for keeping products in warehouses by considering factors such as product diversity. This study used DBSCAN, AGNES, and SOM algorithms to classify products based on 9 features extracted from the products, which resulted in 55 different classification modes with each of the machine learning algorithms. The development of this algorithm aimed to provide factory and warehouse managers with a solution for making more effective decisions in arranging warehouse products.

The competitive landscape among companies and their supply chains necessitates a heightened focus on collaborating with the best suppliers. The appropriate selection of suppliers presents an opportunity for organizations to gain a sustainable competitive advantage while enhancing profitability. The Etka organization, responsible for meeting the consumption and general needs of the armed forces, is no exception. Consequently, it requires establishing partnerships with suppliers across industries. To address this need, this research aims to provide a framework for selecting suppliers with lean, agile, and resilient approaches within the supply chain of the Etka organization. To achieve this objective, an extensive review of the relevant literature on lean, agile, and resilient supplier selection was undertaken. Through this process, key selection criteria were identified, and the fuzzy screening method was employed to localize and refine these criteria. Furthermore, the combined rough best-worst method was utilized to assign weights to each criterion, reducing uncertainties associated with expert opinions. "Cooperation and coordination" emerged as the most critical criterion from the resilient supply perspective, "trust development" from the agile supply approach, and "product quality" from the lean supply approach. The application of the rough VIKOR method then facilitated the ranking of selected suppliers, resulting in the Qaemshahr canning company being identified as the most desirable supplier in the related industry. This study presents a comprehensive framework for selecting lean, agile, and resilient suppliers within the supply chain of the Etka organization, enabling fruitful partnerships that contribute to competitive advantage and overall profitability.IntroductionThe foundational importance of ensuring the timely provision of high-quality sustenance to the armed forces at reasonable costs stands as a cornerstone in bolstering a nation's defense preparedness. The Etka organization, entrenched in the responsibility of orchestrating the seamless delivery of top-tier nourishment to military personnel from farm to table, grapples with the imperative of devising astute supply chain management strategies. As global challenges, such as the ramifications of the COVID-19 pandemic, underscore the criticality of resilient supply chains, Etka's commitment to fortifying its procurement infrastructure gains newfound significance. While Etka cultivates a portion of its food internally, strategic partnerships play a pivotal role in ensuring the efficient fulfillment of diverse demands. In tandem, industry-wide strategies like adopting lean, agile, and resilient supply chain methodologies could guide organizations toward operational efficiency, profitability, and customer-centricity. The nimble nature of the agile approach, coupled with the waste-reducing prowess of the lean strategy, and the resilience to rebound after disruptions embody the ethos underpinning modern supply chain excellence. Effective supplier selection emerges as a linchpin in the quest for operational optimization and enhanced competitiveness, particularly manifesting as an imperative facet in Etka's role as a custodian of the armed forces' nutritional sustenance. This necessitates meticulous scrutiny, evaluation, and collaboration with suppliers aligned with the organization's criteria to ensure streamlined procurement processes. This scholarly endeavor embarks on architecting a comprehensive roadmap for selecting suppliers harmonized with Etka's requisites through the delineation of precise procurement criteria and strategic imperatives.Literature ReviewIn the area of supplier selection, various studies have been conducted. Among these, there are studies that have selected suppliers based on one, two, or all of the essential, agile, and resilient approaches. These three approaches are of significant importance in the matter of selecting suppliers, to the extent that almost all recent research in the field of supplier selection has examined at least one of these approaches. Therefore, in this study, after reviewing and studying the literature on the subject, we proceed to identify the criteria for selecting essential, agile, and resilient suppliers. The identified criteria for selecting essential suppliers include: cost, quality, lead time, collaborative relationships with suppliers, level of service and customer satisfaction, flexibility, just in time, information sharing, implementation of quality management systems, waste management, automatic inventory replenishment, and inventory management. The identified criteria for selecting agile suppliers include: production flexibility, delivery flexibility and speed, resource flexibility, market sensitivity, information sharing, reliability, responsiveness, capacity to create new production lines, process integration through IT, quality improvement, minimizing uncertainty, innovation capability, cost flexibility and reduction, trust development, reducing resistance to change, and improving after-sales services. The identified criteria for selecting resilient suppliers include: excess inventory, reliability, adaptability, multiple sourcing, collaboration and coordination, identifying vulnerable points, awareness of risks and their management, redundancy in production equipment, having a list of alternative materials, technological capability, demand-driven management, and warehouse location flexibility.MethodologyThe current study is characterized by an applied research type with a descriptive methodological approach. The nature of this research as a questionnaire-based inquiry categorizes it as a descriptive-survey study. The statistical population targeted in this investigation comprises experts and managers from the business department of the Etka organization. Data collection methods employed in this study encompass both library research for theoretical foundations and field research for practical investigations. The foundational knowledge and background were cultivated through a meticulous examination of authoritative texts and articles, aligning with the library research method. Conversely, the actual data collection process involved direct engagement with the subjects through the distribution of a questionnaire, reflecting the field research method. Upon establishing the supplier selection criteria derived from existing literature, a questionnaire was formulated to screen these criteria, which was subsequently shared with the experts at Etka organization. Through expert consultation, certain identified criteria deemed less critical for the organization were eliminated following a fuzzy screening process. Subsequently, the best and worst criteria were identified through a second questionnaire distributed among the experts. Using the rough Best-Worst Method (BWM), expert-valued criteria were quantified and prioritized within the lean, agile, and resilient frameworks. Subsequently, a final questionnaire was administered to experts, aiming to evaluate suppliers from the Etka Organization based on the weighted criteria determined in the previous stages. These supplier evaluations were quantified using Raff's numbers, supported by Raff's theory relationships. Finally, a comparative analysis was conducted to rank the selected suppliers utilizing the VIKOR method relationships. This methodological approach employed a systematic process of refining criteria, expert consultation, and quantitative analysis to effectively evaluate and rank suppliers within the organizational context of the Etka organization.ResultsThe findings of this research indicate that when selecting a supplier for the Etka organization, the most critical approaches in order of importance are resilience, agility, and lastly, the lean approach. Consequently, a framework was developed for the selection of suppliers optimized for Lean, Agile, and Resilient (LAR) characteristics within this organization. The key criteria for supplier selection across lean, agile, and resilient approaches were identified as product quality criteria, trust development, and cooperation and coordination, respectively. Moreover, through a comparative analysis of the weight and significance of these criteria, it is evident that among the top five essential criteria recognized, three fall within the realm of resilience. This reaffirms the significance of prioritizing resilient suppliers in the selection process. Lastly, the research findings highlight that the Qaemshahr cannery demonstrates exemplary performance concerning Lean, Agile, and Resilient approaches.Conclusion and DiscussionThe framework devised for selecting suppliers using Lean, Agile, and Resilient (LAR) approaches offers several practical applications for the Etka organization. A comprehensive assessment of the prevailing supply conditions within the organization revealed a minimal adoption of the key criteria outlined in this research in the practical supplier selection processes at Etka. With attention to these findings, the Etka organization stands to enhance its supply chain operations within the food industry significantly by revisiting and fine-tuning its supply policies in alignment with the framework established in this study. The suggested course of action entails the organization reconsidering its supplier selection criteria to prioritize suppliers who align with the identified criteria, fostering improved operational performance. By recalibrating its supplier selection practices in accordance with the research framework, the Etka organization can strive towards optimizing its supply chain operations, enhancing efficiency, and fostering resilience in the face of challenges. Therefore, leveraging the insights gleaned from this research framework presents an opportunity for the Etka organization to refine its supplier selection strategies, bolster operational efficacy, and cultivate relationships with suppliers that align closely with the organization's objectives and requirements.

Since the healthcare system is one of the most important pillars of community health, and considering that providing healthcare services to the people is one of the elements of individual development in any country, attention and supervision of this sector can lead to development and social welfare. To ensure better and higher quality healthcare services, performance evaluation in the health sector plays a crucial role. In order to achieve this, proper and proportional use of existing facilities and assets is inevitable. In this study, by introducing an application in the field of healthcare systems, the educational hospitals of the country have been measured in terms of performance and their managerial ability has been calculated. Additionally, by identifying and introducing the impact of contextual variables on the performance of decision-making units, their efficiency has been assessed. For this purpose, data related to educational hospitals in 31 provinces of the country was collected, and then by identifying contextual variables and with the presence of undesirable factors, the efficiency was evaluated and the managerial ability of each was calculated. To reach this goal, in the first step, technical efficiency with the presence of undesirable factors was calculated using data envelopment analysis technique, and then the logarithm of technical efficiency obtained from the first stage was regressed on a set of contextual variables that affect hospital performance. In the next stage, managerial ability was extracted from the residual of the regression obtained from the previous stage. Finally, a unique ranking based on the managerial ability of each unit was provided. Ultimately, the results obtained were analyzed and examined in order to provide valuable suggestions for managers and more efficient management of the country's hospitals to maintain public health. According to the study, without considering contextual variables, 25 effective units were evaluated, but by applying the effect of contextual variables on the efficiency index, no unit becomes effective, proving the high impact of such indices on the performance of units. Additionally, in the ranking of units based on managerial ability, Lorestan province ranked first and Golestan province ranked last.IntroductionThe issue of increasing productivity and efficiency in healthcare costs is important for all countries. The health sector, by identifying the factors that affect community health precisely, influences national macroeconomic planning and minimizes their adverse effects on health. By utilizing the best practices in healthcare, significant improvements in the health of individuals and communities can be achieved. Therefore, proper investment in healthcare facilities and health centers, as well as improving the quality and efficiency of their services, is essential for sustainable development. In order to increase efficiency and productivity, understanding the current status and measuring the performance of hospitals in the healthcare system is of paramount importance. Ensuring the provision of better and higher quality health services requires evaluating the performance of the healthcare system. Therefore, it seems that employing efficiency measurement techniques and improving performance and productivity in this sector can improve processes and optimize the use of resources and the fair distribution of resources for the provision of desirable services. In recent years, various studies and methods have been proposed by researchers to measure the efficiency of decision-making units, which can be divided into two categories: parametric and non-parametric methods. Farrell (1957) first introduced the non-parametric method, and then Charnes et al. (1978) extended the initial analysis by Farrell from multi-input and single-output to multi-input and multi-output. The model developed by them was named the Charnes-Cooper-Rhodes model. Then, Banker et al. (1984) introduced the model. The non-parametric method is a linear programming-based method in which a linear programming problem is solved for each decision unit. This branch of operations research has rapidly advanced and is called data envelopment analysis. Data envelopment analysis is a mathematical programming technique for evaluating decision-making units and plays a fundamental role in identifying efficient boundaries and measuring the relative efficiency of units under scrutiny. Data envelopment analysis allows for the comparison of units with each other. Considering the importance of the health sector in improving the quality of life for individuals in society, we felt it necessary to examine the performance level and calculate the managerial capacity of hospitals in all 31 provinces of the country to ensure the proper functioning of this sector and take even small steps towards improving the quality of this sector. The aim of this research is to analyze and evaluate the performance of health sector hospitals in Iran in the presence of contextual variables and provide a ranking method based on managerial capacity. For this purpose, data related to educational hospitals in all 31 provinces of the country were collected, and then, by identifying contextual variables and the presence of undesirable factors, an attempt was made to evaluate the efficiency and calculate the managerial capacity of each hospital unit. To achieve this goal, in the first step, technical efficiency with the presence of undesirable factors was calculated using data envelopment analysis technique, and then the logarithm of technical efficiency resulting from the first step was regressed on a set of contextual variables that affect hospital performance. In the next step, managerial ability was extracted from the residual of the regression from the previous step. Finally, a unique ranking based on the managerial ability of each hospital was presented.MethodologyIn this article, based on studies conducted by Demerjian et al. (2020) and Banker et al. (2020), we examine the performance analysis and managerial abilities of 31 hospitals in the country through a three-stage process. Firstly, considering the presence of undesirable outputs, the efficiency analysis of the units of interest is obtained using the efficiency model proposed by Kuosmanen (2005) with the (3) technology. Then, using the least squares method, the impact of each of the contextual variables in this study, including "asset base", "density", and "number of physicians", on the efficiency scores obtained from the first stage is regressed. Subsequently, managerial ability is obtained from the residuals of the previous least squares method. Finally, a unique ranking based on the managerial ability of each hospital is presented.ResultsIn this study, which was conducted on the performance of the health care in Iran, a new ranking based on managerial ability was provided for comparing units. Based on calculations performed on a number of hospitals in 31 provinces of the country without considering contextual variables, 25 efficient units were evaluated. However, by applying the effect of contextual variables on the efficiency index, no unit appears to be efficient, proving the significant impact of contextual variables on the performance of units. Furthermore, the relationship between contextual variables and efficiency index was determined. For example, an increase in the amount of the contextual variable "number of physicians" will lead to an increase in managerial ability. This means that an increase in the number of physicians will benefit the improvement of the system's efficiency and managerial ability.ConclusionWithout a doubt, studying and investing in the healthcare industry is one of the most profitable and best areas for investment. In this regard, government hospitals in each country are one of the main and most important components of the healthcare sector. The hospitals studied in this research are considered as 3 government hospitals per province. Based on past efficiency studies, we find that each decision-making unit had its own specific inputs and outputs. The aim of this study is to analyze and examine the managerial ability of public hospitals in Iran. In this study, the performance of selected hospital units is analyzed in terms of managerial efficiency, considering the impact of other variables known as contextual variables on the performance of a decision-making unit. In this study, the performance of government hospitals in Iran is analyzed from a managerial perspective. The first step involves calculating the efficiency of units using basic models and considering undesirable outputs. Then, in the second step, the logarithm of technical efficiency obtained from the first step is regressed on a set of contextual variables that affect hospital performance. Furthermore, the impact of contextual variables, including total assets, physician density, and number of physicians, on the size of unit efficiency is measured in this study. Based on the results, 25 efficient units were evaluated, but with the application of contextual variables on efficiency indicators, no unit becomes efficient, proving the high impact of such indicators on unit performance. Additionally, based on the calculations performed, in the ranking of units with a managerial approach, Lorestan province ranks first and Golestan province, which has the weakest performance among the units under study, ranks last. The impact of contextual variables on efficiency indicators has been examined. For example, the impact of the "number of physicians" indicator on efficiency is direct, and a one-unit increase in it will lead to an increase in managerial efficiency. This means that an increase in the number of physicians will benefit the system's efficiency and managerial ability. However, the impact of the density variable, unlike the number of physicians, has an inverse effect on managerial ability. To provide suggestions for future studies, one can refer to generalizing the problem to the uncertainty space and studying different applications by bringing the problem into random spaces, providing more predictive predictions. Furthermore, this study can be implemented in analyzing performance and calculating managerial ability in various industries such as power plants, insurance industry, banks, etc., and based on the applications and the type of technology used, different approaches can be provided for calculating managerial

This article examines selection of the cooperation model with the supplier and monitoring the cooperation strategy over time by continuously monitoring the performance of the supplier over time. For this purpose, a multivariate fuzzy profile monitoring model by considering risk conditions in two stages for monitoring the supplier's unforeseen costs. In the first step, criteria are extracted to evaluate the delivery process, quality, as well as the risk function according to library resources and the opinion of experts of the industry. in the first phase of monitoring using the T<^>2 method, the parameters of the model based on successive differences are calculated and monitored separately for the multivariate multiple profile of the delivery process and quality, taking risk into account, and in the second phase of the method The likelihood ratio is used to monitor the profiles in a fuzzy manner over time, so that if there is an alarm, the control chart will show it in the shortest possible time. In the last stage, using the results of the fuzzy profiles and using tool of the fuzzy inference system, it is used to monitor the unforeseen costs and make a decision about the supplier and monitor the cooperation strategy.

The purpose of this research was to identify and analyze the drivers of the smart sustainable circular supply chain. Twelve samples were selected by the snowball method from among industry experts in the edible oil sector. Drivers were finalized with expert opinions and analyzed using the DEMATEL and AISM methods. The results showed that organizational competence and human resource competence are the most effective drivers, while economic sustainability and environmental sustainability drivers have the highest levels of effectiveness compared to other drivers. According to the results of the research, officials in the edible oil industry should pay special attention to organizational competence to advance towards a sustainable and smart circular supply chain, as this driver has the greatest impact compared to others.

This paper presents a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic, incorporating dimensions of sustainability. The objectives of the model are as follows: 1) Minimizing the costs associated with building facilities and waste treatment centers, vehicle fuel costs, and environmental costs due to pollutant emissions; 2) Maximizing the energy generated from the waste combustion process; 3) Minimizing the risk of virus transmission resulting from inadequate waste management; and 4) Maximizing the number of job opportunities in the established centers. It is important to note that existing uncertainties are addressed through the application of fuzzy set theory. Given the multi-objective nature of the model, two multi-objective algorithms, namely the Pareto archive-based Krill Herd Algorithm and Non-dominated Sorting Genetic Algorithm II (NSGA-II), are employed to solve the defined problem. The results indicate that the proposed Krill Herd Algorithm converges to a solution with higher quality and dispersion compared to NSGA-II. Additionally, through a comparison of the spacing index and running time of the two algorithms, it is observed that NSGA-II explores the solution space with higher uniformity and solves the model in less time.IntroductionHospital waste encompasses a broad spectrum of both hazardous and non-hazardous materials. The management of hospital waste involves the development of a suitable supply chain network for handling waste generated in the healthcare sector. Improper disposal or mishandling of contaminated waste not only contributes to environmental pollution but also poses a risk of transferring viral pathogens to healthcare and recycling personnel. Research has shown that inadequate disposal of medical waste can lead to the transmission of up to 30% of hepatitis B, 1-3% of hepatitis C, and 0.3% of HIV infections from patients to healthcare workers. This paper aims to design a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic while considering the dimensions of sustainability.Literatur ReviewIn recent years, various studies have delved into the complexities of medical and hospital waste management, proposing mathematical models to address this intricate issue. The current study is built upon the work of Valizadeh et al. (2021). In their paper, a hybrid mathematical modeling approach was introduced, featuring a Bi-level programming model specifically tailored for infectious waste management during the COVID-19 pandemic. The outcomes revealed that, at the higher level of the model, governmental decisions aiming to minimize total costs associated with infectious waste management were crucial. This involved the conversion of collected infectious waste into energy, with the generated revenue being reinvested back into the system. The findings indicated that, through energy production from waste during the COVID-19 pandemic, approximately 34% of the total costs related to waste collection and transportation could be offset. The uniqueness of this study lies in its consideration of three sustainability dimensions: risk, vehicle routing, energy production, employment, and emission of polluting gases. Consequently, the novelty of this research, when compared to previous studies and the article by Valizadeh et al. (2021), is evident in several aspects. It introduces an integrated multi-objective positioning-routing model for the supply chain of waste management under pandemic conditions, taking into account sustainability dimensions, notably the economic aspect, and employs meta-heuristic algorithms for model resolution.MethdologyTo ensure the proper management of hospital waste, the waste is categorized into two groups: infectious and non-infectious waste. It is assumed that waste in hospitals and health centers is segregated and placed in infectious and non-infectious waste bins. The collected waste undergoes further processing: infectious waste is transported to incineration centers, where it is burned and converted into electrical energy, while non-infectious waste is sent to waste recycling centers, where it is reprocessed and returned to the production cycle in the industry. A multi-objective mathematical model is presented to integrate location-routing decisions in the supply chain of hospital waste management, with the following modeling assumptions:Waste segregation at the source helps prevent all waste from becoming viral, reducing the spread of viruses through waste.The risk of spreading viruses is assumed to be relatively equal for each type of waste.Two types of vehicles are considered for transporting waste: the first type carries non-infectious waste, while the second type carries infectious waste.The number of cars, waste collectors, and the capacity of waste incinerators are considered constant in this study.The mathematical model is multi-objective, with the objectives being to optimize the three dimensions of sustainability (economic, social, and environmental).The economic goal is to minimize system costs, including the cost of site location, recycling, collection, segregation of non-infectious waste, and incineration.The environmental goal is to minimize the emission of pollutants in the transportation and processing system in various facilities, as well as to maximize the production of electrical energy.The social goal is to minimize the risk of virus transmission and maximize the employment rate.Results and DiscussionThis research presents a multi-objective mathematical model for the reverse supply chain of hospital waste management during the COVID-19 pandemic in Iran and solves it. The pandemic period is considered a time of maximum utilization of health centers and waste disposal. In this context, a three-objective mathematical model was initially introduced. To solve the model, the krill herd optimization algorithm was employed. The performance of the krill herd optimization algorithm was scientifically and practically evaluated by comparing it with the well-known NSGA-II algorithm. After designing the model, both the multi-objective krill herd algorithm based on Pareto Archive and the NSGA-II algorithm were utilized to solve the model. The results of solving the model demonstrated that the proposed krill herd algorithm, designed in combination with VNS, effectively solved the model and determined the optimal solution within a boundary. Comparing the results of this algorithm with those obtained by the renowned NSGA-II algorithm revealed that the krill herd algorithm produced solutions of much higher quality.ConclusionThe comparison of the Index of dispersion between the two algorithms indicates that the krill herd optimization algorithm explores more points in the solution space, leading to a lower probability of getting stuck in local optima compared to the NSGA-II algorithm. On the other hand, the index of uniformity for the NSGA-II algorithm is lower than that of the krill herd algorithm (lower values are better), suggesting that the multi-objective genetic algorithm explores the solution space more uniformly. Considering the execution time of the two algorithms, it was observed that the NSGA-II algorithm solved the model in less time. Additionally, the increasing trend of execution time in both algorithms confirms the NP-HARD nature of the hospital waste management problem. According to the output of the MATLAB software, considering the presented model, the results affirm the capability to optimally select hospital waste recycling centers.

The current study aimed to cluster the application of digital technologies from Industry 4.0 in the agricultural food distribution network. To achieve this goal, a bibliometric technique was employed to identify prominent trends and themes in this field through the analysis of articles, authors, countries, and co-citations of authors and bibliographic pairs. Through an extensive search in the Scopus scientific database, bibliographic information for 331 valid and relevant scientific articles was acquired. This information was inputted into the bibliometric package in R software, and the most influential journal, author, university, country, and most cited authors were determined. To visualize the information, Vosviewer software was utilized for co-citation analysis of authors, cited references, and bibliographic pairs. The findings from the network analysis revealed that the studies on the application of digital technologies in the agricultural food distribution network can be categorized into five main clusters.IntroductionIndustry 4.0, viewed as a new industrial stage, has introduced complex information and communication technologies that facilitate comprehensive connections across different parts of the supply chain. The digital technologies associated with Industry 4.0 allow production lines, business processes, and teams within a supply chain to collaborate seamlessly, irrespective of location, time zone, network constraints, or any other factors. Researchers highlight that the advent of digital technologies from the fourth industrial revolution, including radio frequency identification, big data, cloud computing, smart sensors, machine learning, robotics, augmented production, artificial intelligence, augmented reality, the Internet of Things, blockchain, and similar technologies, holds immense potential for significantly enhancing production productivity. These technologies could lead to substantial innovation, competitive growth, and may contribute to improving the sustainability of the current industrial system. To meet the escalating demand for food, agricultural marketing professionals and managers globally must maximize the efficiency of the agricultural distribution network, given the widespread adoption of digital technologies. The increasing significance of this goal has prompted marketing researchers to explore the use of digital technologies in the agricultural food distribution network, leading to a substantial number of studies in this research field since 2011. In this context, the present study aimed to cluster the utilization of digital technologies in Industry 4.0 within the agricultural food distribution network. A bibliometric study was conducted to identify existing gaps in research and propose future directions. The research focuses on the application of digital technologies in the distribution network.Aligned with the research objective, fundamental questions are posed: Which publications, authors, and countries are most influential in the application of Industry 4.0 digital technologies in the agricultural food distribution network? Additionally, what scientific clusters exist in this domain?MethodologyThe objective of the current research is to conduct a bibliographic analysis of studies related to the application of digital technologies in Industry 4.0 within the agricultural food distribution network. Utilizing bibliometric techniques, a crucial measure for evaluating scientific output, a comprehensive examination of scientific literature was carried out concerning the application of digital technologies in Industry 4.0 within the agricultural food distribution network.The search was conducted within the Scopus scientific database, which encompasses a significant array of diverse journals and authoritative articles globally. The search covered three sections: title, abstract, and keywords, yielding a list of studies that exclusively included English-language articles from journals (excluding conference studies and book chapters) published between 2011 (the inception year of Industry 4.0) and 2023. By imposing these criteria, 352 original pieces of data containing bibliographic information were obtained. Subsequently, the title and abstract of each article were meticulously scrutinized to identify information relevant to the agricultural food distribution networks. Among these, 6 articles pertaining to the halal supply chain and 15 articles conducted as systematic reviews were excluded from the bibliographic information collection. The final portfolio for analysis consisted of bibliographic information from 331 articles, which was then entered into the bibliometric software package. This analysis was carried out using R software and VOSviewer software. The bibliometric software package facilitated quantitative bibliographic analysis, while the VOSviewer software was employed for visualizing and analyzing citation networks.ResultsThe quantitative findings indicate a significant increase in studies related to the adoption of digital technologies in the agricultural food distribution network, particularly after 2017. The most widely utilized digital technologies in the food distribution network include blockchain, the Internet of Things, simulation, artificial intelligence, big data, machine learning, 3D printers, sensors, and digital twins.Through the analysis of bibliographic pairs, five primary clusters were identified concerning the application of digital technologies in the agricultural food distribution network. These clusters are associated with the use of digital technologies in ensuring food quality, enhancing distribution network flexibility, establishing modular architecture within the distribution network, implementing intelligent logistics systems, and promoting sustainable distribution networks.ConclusionBased on the themes of the clusters identified in Table 7, it can be concluded that the Internet of Things and blockchain play crucial roles in real-time tracking, tracing, and monitoring of food throughout the supply chain, thereby reducing wastage. RFID technologies and digital twins are highly effective in ensuring food safety and facilitating delivery to consumers, especially in the face of environmental changes and crises such as epidemics. Another application of digital technologies lies in the modular architecture of the food distribution network. Through the use of modular architecture, various technologies can modularize tasks and extensive operations within the food distribution network. Ultimately, all these components can be centralized under blockchain technology, with diverse data stored in a vast cloud space. Consistent implementation of digital technologies in the food distribution network has the potential to establish regional warehouses, resulting in reduced distribution and delivery costs, enhanced food safety and sustainability, and the possibility of customizing food for end consumers. This, in turn, will contribute to the stability of the food network.

To explain a framework for managing the risk of transportation in the food industry supply chain, the initial step involves identifying 12 risks that can potentially lead to transportation disruptions, based on the research background. Utilizing the Delphi method and gathering opinions from 15 academic and industrial experts across 3 stages, the risks were ultimately defined. Furthermore, expert opinions were sought to determine solutions to address the identified risks. The grey DEMATEL method was employed to investigate the interaction of risks. The findings revealed that weather problems, natural disasters, insufficient skilled labor/labor strikes, infrastructure capacity, and inflation and exchange rate changes are among the risks that exert a more significant influence on other risks than they are influenced by them. Subsequently, using the grey COPRAS method, the prioritization of solutions to mitigate the identified risks, based on expert opinions, was undertaken. The results indicated that the top-ranked solution is the definition of key performance indicators. Therefore, it is recommended to managers that, in order to establish a robust supply chain and proactively manage risks, they should identify stakeholders and critical processes. Afterward, an agreement on the financial flow in each situation should be obtained, and a value flow map drawn. This approach enables the implementation of preventive measures to reduce supply chain risk and facilitates the preparation of an emergency plan for unforeseen conditions, thereby enhancing resilience.IntroductionDisruption in transportation stands as the pivotal factor undermining the efficiency of the supply chain. Any significant interruption can result in delays or business flow cessation, leading to consequential impacts (Ali et al., 2021). The transportation supply network is susceptible to various technical, economic, and environmental factors (Tan et al., 2023). Simultaneously, research indicates that factors such as a workforce lacking sufficient skills, suboptimal selection of service providers, traffic accidents, and the inability to predict the systemic impact of these risks play a crucial role in transportation, causing disruptions in the flow. In addition to these factors, it is noteworthy that supply chain management in the food industry introduces its own complexities. Unlike other industries, the quality of products in this type of supply chain consistently diminishes during product movement and this issue of perishability intensifying the need for transportation risk management (Hosseini-Motlagh et al., 2019; Choe et al., 2021). Given this context, emphasis should be placed on establishing distribution channels with lower costs and implementing change management to enhance efficiency. However, existing studies have predominantly focused solely on analyzing transportation disruption within companies' supply chains. Clearly, it is insufficient to only address disorders or the risks that may lead to their occurrence. A comprehensive examination of the cause-and-effect relationships among these risks facilitates a systematic understanding of the risk network. This approach enables the development of a more effective program to enhance the resilience of the transportation system. Even in the face of risks and disruptions, this ensures minimal damage, a swift return to normal operational levels in the supply chain, or the application of knowledge management to learn from experiences and prevent the recurrence of disruptions through appropriate implementation solutions. Consequently, the overall performance of the supply chain can be improved. In consideration of these elements, this study aims to address the following main questions:- What are the risks associated with transportation in the food supply chain?- What are the pertinent solutions, according to expert opinions, and what is their prioritization?MethodologyIn terms of its objective, the current study falls within the domain of applied research as it aims to discover practical solutions to address a real-world problem. Regarding information collection, the study is categorized as survey research, wherein data is gathered based on the opinions of 15 experts. Among these experts, 9 are drawn from the industrial community, each possessing over 10 years of experience in the commercial sector and raw material procurement within the food industry. The remaining 6 experts are affiliated with the academic community and have published numerous articles in the field. The study unfolds in several stages. Initially, a compilation of transportation risks causing disruptions in the food supply chain is accomplished through a literature review. Subsequently, the Delphi method is employed to screen and refine these disruptions. The cause-and-effect relationships among the identified disruptions are then scrutinized using the Grey DEMATEL method. Experts are engaged to contribute not only by assessing the risks but also by providing their insights into coping strategies based on their experience. Finally, the coping strategies are prioritized using the Grey COPRAS method.Results and DiscussionAccording to the obtained results, it is evident that the climate problems risks of failure to choose logistics service providers that care about sustainability principles (C6) and frequent change of product delivery time (C9) exhibit the highest degree of interconnectedness with other risks. The weights of these risks have also been determined. Notably, the failure to choose logistics service providers committed to sustainability principles has secured the top rank with a weight of 0.1443. Following closely, the frequent change of product delivery time holds the second position with a weight of 0.1384, while natural disasters rank third with a weight of 0.1039. Turning to coping strategies, it is noteworthy that the solution of "Definition of Key Performance Indicators (KPI)" has claimed the top position. In today's business landscape dominated by Logistics 4.0 and Omnichannel, simplifying processes can create significant added value for any business, particularly in minimizing transfer time. Concurrently, many manufacturing companies are leveraging various logistics transportation modes as a critical factor for promptly responding to demands, thereby enhancing service reliability and minimizing travel time (Foroozesh et al., 2022). The adoption of modern technologies such as the Internet of Things facilitates real-time inventory monitoring, contributing to dynamic pricing policies. As product quality diminishes along the chain, electronic labels enable adjusting product prices based on features (Kumar & Agrawal, 2023).ConclusionStudies indicate that supply chain managers, particularly in food supply chains, have demonstrated significant commitments to sustainability goals, leading to the pursuit of a diverse array of performance improvement projects. This study identifies various risks and corresponding coping strategies. Outsourcing logistics activities to 3PL allows leveraging their expertise in supply chain management, thereby enhancing stability and efficiency. This approach can contribute to reducing the carbon footprint, increasing order fulfillment, and lowering energy consumption throughout the supply chain.For future research endeavors, it is recommended to prioritize strategies related to realizing the circular economy within the logistics system of the food industry. Providing a roadmap for the sustainable development of logistics clusters can enhance supply chain performance, minimize waste, and boost the social credibility of the supply chain. Additionally, attention to the concept of greenwashing in sustainable logistics, particularly concerning the fulfillment of social responsibility, can prove beneficial in improving overall supply chain performance

The purpose of this article is to design a model for improving the sustainable performance of small and medium food companies in Guilan Province. The aim is to provide managers and decision-makers with insights into the factors, challenges, and consequences associated with enhancing the sustainable performance of these companies in the current business environment. The article falls under the category of applied research in terms of its purpose, exploratory-descriptive research in nature, and field studies based on data collection. It is classified as qualitative research. The Grounded Theory method was employed to create a paradigm model, progressing through three stages of open, axial, and selective coding. The results of data analysis using Grounded Theory led to the identification of 13 main floors, 29 main categories, and 248 concepts. The paradigm model, developed through theoretical coding, encompasses six main classes: pivotal phenomenon (sustainable performance improvement), causal conditions (organizational survival, social and environmental obligation, gaining competitive advantage), contextual conditions (environmental factors, organizational factors), interfering conditions (environmental factors, organizational factors), and strategies (macro level, organizational level). Finally, the economic, social, and environmental consequences of achieving sustainable performance improvement were derived through the implementation of these strategies. Introduction The increasing societal focus on sustainability necessitates attention to improving sustainable performance in small and medium-sized companies. Today, achieving a competitive edge and attracting customers goes beyond operational or financial superiority. In the contemporary business landscape, companies are expected to demonstrate responsibility and consider future generations in their activities. SMEs play a pivotal role in the industrial growth of developing economies globally. Enhancing communication and information flow in small and medium-sized companies can lead to more efficient processes and cost reduction. To thrive in the current business environment, SMEs must adopt emerging technologies to improve their sustainable performance. Industry 4.0 and the circular economy, which involve the use of advanced technologies like artificial intelligence and the Internet of Things, can be instrumental in achieving sustainable performance in these companies. The synergy between Industry 4.0 technologies and circular economy methods positively contributes to enhancing sustainable performance. Given that small and medium industries are significant drivers of economic growth in regions like Guilan province, improving the sustainable performance of companies in this sector can have a substantial impact on preserving and maintaining environmental aspects. With the rapid growth of technology and the advancement of Industry 4.0, SMEs are recognized as vital economic foundations contributing to the development and economic growth of societies. It is crucial to investigate and conduct research in the field of Industry 4.0 and the circular economy to improve the sustainable performance of small and medium-sized companies. A conscious analysis of the benefits and challenges of Industry 4.0 on SMEs allows for a better understanding of the needs and opportunities within this sector. Examining the effectiveness and utilization of new technologies in the processes and organizational structures of small and medium companies can lead to increased efficiency, cost reduction, improved quality, enhanced decision-making processes, and expanded market presence. Literature Review Performance, in a broad sense, can be defined in accordance with the concept of quality and an organization's ability to achieve internal and external goals. It is important to note that performance encompasses multiple dimensions. Sustainability, on the other hand, is a multidimensional concept that presents a significant challenge in our time. It involves the understanding and management of economic, social, and environmental performance simultaneously. Researchers argue that if the current population and economic growth rates persist, the utilization of the planet's natural resources will surpass its capacity. This issue gives rise to environmental protection concerns, which are addressed under the umbrella of sustainable development. Sustainable development is defined as development that meets the needs of the present without compromising the ability of future generations to meet their own needs. In a broader sense, a sustainable development strategy entails creating harmony among humans and between humans and nature. It implies that sustainability requires a societal approach that manages the environmental perspective alongside the economic perspective in the progress of development and performance improvement. In the present era, there is an expectation from customers and members of society for companies and organizations to be responsible and consider future generations in their activities and operations. Prioritizing the future generation in activities signifies a positive step towards sustainable performance and demonstrates the organization's commitment to a sustainable global economy. While the concept of sustainability is easily understood, operationalizing and concretely expressing it poses challenges. Decision-makers in industries need to evaluate and review their operations considering both internal and external effects. Optimal decisions can only be made when the social, economic, and environmental consequences are taken into account. Methodology The Research falls under the category of applied research in terms of its purpose, exploratory-descriptive research in nature, and field studies based on data collection. Grounded theory, a qualitative research approach, is particularly well-suited for exploratory research seeking to theorize or identify patterns. It employs open, axial, and selective coding methods for data analysis. This approach, introduced by Strauss and Corbin in 1990, is based on the three-stage coding process of open, axial, and selective (or theoretical) coding. In this regard, it utilizes the logical paradigm or diagrammatic representation of the theory created. Research based on the grounded theory approach, employing a systematic strategy, culminates in the development of hypotheses and statements that may specify the relationships between classes in the axial coding paradigm. Discussion and Results The central category identified is Sustainable Performance Improvement. Two dimensions associated with this central category are Industry 4.0 and the circular economy. The most crucial factors influencing sustainable performance improvement are categorized into three groups: organizational survival (including profitability and maintaining market share), social and environmental requirements (such as attracting public support, considering future generations, and managing limited resources), and gaining competitive advantage (encompassing both temporary and sustainable competitive advantages). These factors contributing to sustainable performance improvement are further classified into two categories: environmental factors and organizational factors. Environmental factors involve challenges such as environmental instability, legal factors, and political factors, while organizational factors encompass financial and cost considerations, along with human resources. Strategies for achieving desirable outcomes are outlined at both the macro level (involving government and industrial estate company activities related to consortium formation, culture, and education) and the organizational level. Small and medium-sized food industry companies are encouraged to pursue innovation, creativity, value chain knowledge, and professional structure to create favorable benefits for themselves. The final segment of the sustainable performance improvement model outlines economic, social, and environmental consequences. Key consequences at the economic level include company development, sustainable profitability, and sustainable productivity. At the social level, consequences encompass the welfare of workers and the social responsibility of the organization. The most critical consequence at the environmental level is the achievement of a sustainable environment. Conclusion To design a model for enhancing the sustainable performance of small and medium-sized companies, the grounded theory methodology with the systematic approach of Strauss and Corbin was employed. This involved conducting interviews with experts to gather the necessary conceptual codes. Following the systematic approach, data analysis utilized three stages: open coding, axial coding, and selective coding. The design of the model adhered to the paradigm model of this approach, incorporating six dimensions: the central category, causal conditions, contextual conditions, intervening conditions, strategies, and consequences.

In this study, a comprehensive classification for supply chain risks in the pharmaceutical industry is presented using the Bailey’s classical strategy method and the four-stage Collier method. Initially, through the examination of texts related to the main hazard groups, supply chain elements, considering resources and functions, and categorizing upstream supply chain organizations, primary industry, and downstream supply chain organizations within the industrial and market environment, infrastructural environment, and external macro environment were modeled. In the next stage, criteria related to the security and safety of the supply chain were identified. In the final stage, a two-dimensional matrix classification for the identification of supply chain risk factors was proposed through the cross-tabulation of supply chain elements with security and safety criteria. Based on this classification and utilizing the exemplification method through a synthetic framework, a detailed list of risk factors was compiled. The aim of this study is to propose a comprehensive risk classification for pharmaceutical industries.MethodBailey’s classical strategy method has been used to develop a comprehensive classification of supply chain risks in pharmaceutical industries. In order to review the existing knowledge about supply chain risk groups, a systematic review of literature was performed. In the first stage, to find articles related to supply chain risks in the pharmaceutical industry, different combinations of related keywords have been used to search for articles in relevant databases. The selected articles were examined in three stages: extracting and classifying the main risk groups of the supply chain (the first dimension of the conceptual framework of classification), extracting and classifying criteria for a low-risk supply chain (the second dimension of the conceptual framework of classification), and applying the two-dimensional framework of classification to identify and classify risk factors of the supply chain.ResultsA total of 77 articles were selected for review. Based on the analysis of these articles, 83 risk groups were identified. These risk groups were arranged into a model including upstream supply organizations, the main industry, and downstream supply organizations, considering the relationships between supply chain’s resources, functions, and outcomes in the industry and market environment, infrastructural environment, and external macro environment. In the next step, 30 criteria for a safe and secure supply chain were identified. These criteria are divided into two general categories: criteria for the security of the internal supply chain environment (criteria of resistant supply chain resources and criteria of resilient supply chain functions) and criteria for the safety of the external supply chain environment (criteria of safety of market and industry, criteria of safety of infrastructural environment, and criteria of safety of external macro environment). In the last stage, through cross-tabulation of resource groups with resource resistance criteria, function groups with function resilience criteria, and peripheral environment elements with peripheral environment safety criteria, a model for identifying risk factors in the industrial environment was proposed. Based on this model, 372 risk factors of the supply chain of the pharmaceutical industry were identified.ConclusionIn this study, a new classification for supply chain risks of the pharmaceutical industry has been presented. The proposed classification is highly comprehensive, and the number of risk groups counted in this study is more than all the studies that have been done in this field so far. Most existing risk taxonomies are incomplete and do not follow a specific theoretical model. The classification of risk groups identified in this study has been done based on a model that considers the relationship between assets, functions, and outcomes of the supply chain. The risk groups identified in this study cover from the upstream of the supply chain to the main industry and the downstream of the supply chain. Many risk taxonomies focus on the pharmaceutical industry and do not cover the entire supply chain from raw material production to customers. In this study, cross-tabulation of resource groups with resource resistance criteria, function groups with function resilience criteria, and peripheral environment elements with peripheral safety criteria create an ideal model for identifying risk factors in the industrial environment. The classification proposed in this study can be used to evaluate the resistance and resilience of the supply chain. This model can also provide a suitable basis for identifying and evaluating risks in the supply chain environment. In addition, results of this study provide a very practical guide for choosing supply chain risk management strategies.

In today's industrial units, operators monitor equipment performance, and the challenging coordination between units in vast operating environments with high volumes of equipment can lead to irreparable damage. Despite considerable technological advancements in inspection and surveillance, this responsibility can be effectively delegated to smart devices and the Internet of Things (IoT). Furthermore, the emergence of "edge computing" technology has prompted researchers to explore edge-based computing designs due to their numerous benefits. This study presents a combined model of IoT and civilian drones for intelligent monitoring of industrial equipment performance, employing an edge computing approach. The model is specifically investigated through a case study involving wind turbines. The model evaluates the performance of drones for intelligent monitoring of wind turbines in three stages: 1) Detection process, 2) UAV computational evacuation process, and 3) UAV local computation process. Given the dual purpose of the final model, which involves a combination of the aforementioned three steps, a genetic method was employed for problem-solving with negligible sorting. The amplified epsilon restriction method, utilizing random numbers, was also considered, but the combination of genetic and negligible sorting methods outperformed it, particularly in large problems where the enhanced epsilon restriction method struggled to provide timely responses due to the inherent complexity of the problem. IntroductionToday, in various industries, the productivity and efficiency of equipment contribute to the advancement of production and the profitability of production units. Beyond repair costs, equipment breakdowns also result in the expense of lost opportunities for the production unit. Without a solution to prevent these costs, bankruptcy for production units becomes a real possibility. Therefore, consideration should be given to a solution for the optimal monitoring of equipment. Clearly, swift action is crucial when any equipment is damaged, and such rapid response is unattainable through human effort alone. Despite significant technological advances in inspection and monitoring, this task can be delegated to smart tools and the Internet of Things (IoT). The IoT is regarded as one of the most crucial factors for the prosperity and progress of today's and future industrial businesses. Modernizing equipment is a priority for today's industries to quickly adapt to the evolving market changes and harness existing technologies. Businesses incorporating IoT into their infrastructure experience substantial growth in areas such as security, productivity, and profitability. As the use of industrial IoT increases, productivity levels in industries are naturally expected to rise. The IoT can accumulate massive amounts of information and data, enabling factories and companies to optimize their systems and equipment without being hindered by technological and economic limitations. However, a challenge arises from the substantial volume of data generated by the IoT, which is sent to cloud computing centers for processing. Centralized (cloud) processing results in high communication delays and lowers the data transfer rate between IoT devices and potential users, creating operational challenges in the network. To address this issue, the concept of edge computing has been proposed. Edge computing allows IoT services to process data near their own data sources and data sinks instead of relying on the cloud environment. This approach leads to reduced communication delays and more efficient utilization of computing, storage, and network resources. It also minimizes execution time and energy consumption, proving to be highly beneficial for IoT applications. Consequently, with the advent of "edge computing" technology, many researchers have embraced edge computing-based designs due to its numerous advantages.Materials and Methods In this research, a combined model of the Internet of Things and civilian drones was presented for the intelligent monitoring of industrial equipment, utilizing an edge computing approach. The model was investigated through a case study involving wind turbines. The performance of UAVs for intelligent monitoring of wind turbines was examined in three stages: 1) Detection process, 2) UAV computational evacuation process, and 3) UAV local computing process. Given the dual purpose of the final model, which involved a combination of the aforementioned three steps, the model was addressed using genetic methods with sparse sorting and the enhanced epsilon constraint method employing random numbers. The genetic method with sparse sorting outperformed the enhanced epsilon limit method, particularly in problems with large dimensions. The complexity of the problem made it challenging for the enhanced epsilon constraint method to provide timely responses in such cases.ResultsThe findings of this research offer valuable insights for the effective and accurate management and monitoring of industrial equipment across various industrial units, aiming to optimize costs, quality, and inspection time. Additionally, this research can provide guidance in considering regulatory restrictions in equipment placement before constructing an industrial unit. During the equipment arrangement phase, the model presented in this research can be utilized for optimal energy consumption and time management. As the combined model of the Internet of Things and civilian drones for intelligent monitoring of industrial equipment is a novel concept in the literature, there exist numerous opportunities for further development in this field. This may include the application of the model in additional case studies, such as enhancing the intelligent monitoring of power supply systems, fire services, etc. Moreover, there is potential for refining the mentioned model under conditions where drones operate simultaneously without a specific sequence.ConclusionFailure to monitor industrial equipment properly can result in substantial financial losses for factories and production units. The improper operation of equipment may lead to complete failure, necessitating the need for replacement. Additionally, increased equipment downtime, quality issues, reduced production speed, safety hazards, and environmental pollution can be consequences of equipment failure, ultimately diminishing the profitability of the production unit. Considering factors such as embargoes, emphasis on domestic production, and self-sufficiency, accurate supervision becomes economically crucial for factories.Effective management of the proper operation of industrial equipment is a fundamental requirement for every production unit, given that industrial equipment represents a significant investment for the unit. If device maintenance is limited to repairs only after breakdowns occur, production devices will consistently face unexpected halts, preventing production productivity from reaching its predetermined goals. Therefore, designing a framework for the "intelligent monitoring of the performance of all relevant industrial equipment" stands as one of the most crucial actions for any production unit. Depending on the type of equipment, monitoring the performance of industrial equipment may encompass periodic inspections, maintenance and repair planning, and scheduling the optimal operational time for the equipment