hamed nozari

Artificial intelligence (AI) is assuming an increasingly pivotal role in marketing, as evidenced by its extensive implementation across a diverse array of sectors. Review studies are indispensable across all scientific disciplines, particularly within emerging fields, as they provide scholars and practitioners with insights into the current state of knowledge and prospective avenues for development. In this context, the primary objective of the present study is to examine the social and conceptual framework underpinning the application of artificial intelligence in marketing. To achieve this goal, all bibliographic data up to 2022 were retrieved and analyzed using VOSviewer software. This analysis encompasses descriptive statistics, keyword co-occurrence analysis and co-authorship analysis. The descriptive analysis identifies the most highly cited papers, authors, countries, universities, and journals within the field. The co-authorship analysis reveals the social structure, emphasizing collaboration patterns among researchers. Additionally, the keyword co-occurrence analysis provides insights into the conceptual framework of the field, particularly by highlighting recent research topics and their temporal trends. The findings indicate that AI has become an essential and important tool for businesses to identify and understand customer behavioral patterns and needs, particularly throughout the customer journey and in enhancing customer experiences. These technologies not only support businesses in optimizing their strategies but also assist customers in their decision-making processes.

Evapotranspiration that includes evaporation from the soil surface and transpiration from vegetation, is one of the most important factors of water loss. Also, it is one of the most effective components of the water balance in a catchment in arid and semi-arid regions of the world. Therefore, it is an important physical parameter for water resource management and determining the plant water requirement in the agricultural sector. so far, many experimental methods have been proposed to calculate evapotranspiration, but, they are only suitable at the local scale and cannot be generalized to large areas due to regional dynamics and changes. whereas the accurate estimation of it is also very difficult and expensive, Therefore, in the present study, calculated the amount of evapotranspiration in the irrigated agricultural sector by using of landsat 8 satellite images and Surface Energy Balance Algorithm (SEBAL) in Nahavand Plain. in SEBAL algorithm by estimating all energy components on the earth's surface, including net radiation flux, soil heat flux, and sensible heat flux and using the energy balance equation, evapotranspiration is calculated. Remote sensing also has the ability to show evapotranspiration spatial distribution in addition to estimating the amount of its, because, it is the only technology that extracts factors such as surface temperature, albedo coefficient and plant index in a way compatible with the environment and is also economically affordable.

in this research, in order to estimate daily actual evapotranspiration of the irrigated agricultural and gardens of Nahavand Plain, extracted irrigated agricultural land use map by using of Sentinel 2 satellite images, Then, by using of Landsat 8 satellite images (13 images, from 13 April to 22 October during the growth period of the irrigated crops) and Surface Energy Balance Algorithm (SEBAL), evapotranspiration maps were obtained during the irrigated crops growth period in 2021. These Landsat 8satellite images are obtained by the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard the satellites and are widely used for water resource applications. The OLI sensor has 9 bands and the TIRS has two bands (10th and 11th are the thermal bands). Landsat images are at intervals of 16-days with a spatial resolution of 30 m. In all images, the imaging time was 7:21. Then, due to FAO- Penman monteith method is one of the most important and reliable reference methods in evapotranspiration calculations, in this research, this method was used as a basis for evaluation and comparison. Finally, in order to evaluate the efficiency of SEBAL method in estimating the actual evapotranspiration of irrigated crops and gardens in Nahavand Plain used RMSE function (Root Mean Squares of Errors).

According to the results of the SEBAL algorithm, the highest mean of actual evapotranspiration was related to images 2021.09.04 and 2021.08.19 which fall in the middle of the growing period of irrigated crops. In addition, the surface albedo is noted to be relatively low for these days with the high NDVI values indicating high absorption of radiation by the vegetation during this period. Net solar radiation is directly contingent upon the incoming longwave and shortwave radiations, both of which directly influence the surface temperature. Therefore, areas with higher surface temperatures have higher net solar radiation. The net radiation flux has a direct relationship with NDVI, Greenness, and wetness parameters and is inversely related to albedo, Brightness, and Ts. The vegetative moisture and sensible heat flux are higher on days with high NDVI. Higher NDVI values are an indication of an increase in vegetation greenness, therefore essentially an increase in evapotranspiration is expected to be observed. The lowest mean of actual evapotranspiration is related to the northeast of the case study; due to lack of sufficient surface and ground water resources and consequently the reduction of agricultural lands in this region. Finally, in order to investigate the accuracy of SEBAL method in calculating evapotranspiration, compared the results of SEBAL method with the results of FAO- Penman monteith method. The results of this comparison showed that the SEBAL method with RMSE 0.82 has appropriate efficiency for estimating evapotranspiration.

Due to an increase in population and shortage of water resources, especially in the agricultural sector, researchers are looking for ways to better manage the available water resources. Evapotranspiration rate is one of the most important components of the global hydrologic cycle and has a significant influence on energy balance and climate. the using of indirect methods such as remote sensing can be an important step for estimating the water need of agricultural products, planning and management the country, s water resources. Therefore, according to the position of Nahavand city as the agricultural hub of Hamedan province, in this study, the actual evapotranspiration of the irrigated agricultural land use using of landsat 8 satellite images and SEBAL Algorithm was investigated in this area. According to the results of the SEBAL algorithm, the highest mean of actual evapotranspiration in all of the investigated images is related to the southeast and center of the studied area. that the reason of this matter is location of this area in the main branch of the Gamasiab River and focused the irrigated agricultural and gardens in this area. The final results of this research indicated high precision of SEBAL algorithm in estimating evapotranspiration. Thus, the high accuracy and low error indicate that the SEBAL method could be aptly used to estimate evapotranspiration on a regional scale, in the respective time range. Also, the results obtained from the SEBAL method assisted in understanding the spatial and temporal changes in different stages of plant growth.

This paper introduces the Greedy Man Optimization Algorithm (GMOA), a novel bio-inspired metaheuristic approach for solving complex optimization problems. Inspired by competitive individuals resisting change, GMOA incorporates two unique mechanisms: MMO resistance, which prevents premature replacement of solutions, and periodic parasite removal, which promotes diversity and avoids stagnation. The algorithm is evaluated on standard benchmark functions, including Sphere, Rastrigin, Rosenbrock, and Griewank, and its performance is compared with established algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Ant Colony Optimization (ACO). Results demonstrate that GMOA outperforms these methods in terms of solution quality, convergence rate, and robustness. Statistical significance tests validate the reliability of the results. GMOA’s ability to balance exploration and exploitation makes it a promising tool for various real-world applications, including supply chain optimization and healthcare resource allocation.

Groundwater sources are considered to be one of the most important available sources of fresh water in the world. Today, due to the changes in weather, climate change, population increase and excessive withdrawals of underground water, these resources have faced a significant decrease. Considering that Iran is located in a dry and semi-arid region, the underground water level has undergone many changes in many areas. The level of underground water in Hamadan-Bahar plain area has also faced a significant decrease. Therefore, the prediction of underground water levels in Hamadan-Bahar plain and the management of effective factors in its reduction are the main goals of this research.

In the first step, in this research, it was tried to predict the underground water level with the help of support vector machine integrated model with Simulated annealing algorithm (SVM-SA) using the rainfall values of 4 synoptic stations of Aghkahriz, Ekbatan Dam, Kooshkabad and Marianaj. The uncertainties of this model are also analyzed. In the next step, the precipitation values of the mentioned 4 synoptic stations were predicted for 5 years monthly and annually with the help of seasonal autocorrelated moving average (SARIMA) and autocorrelated moving average (ARIMA) models, and finally, using the predicted rainfall values, the underground water level was predicted monthly and annually using the SVM-SA model for 5 years.

The estimated values of underground water level were analyzed with the help of SVM-SA model using the indices of explanation coefficient (R2), root mean square error (RMSE) and Nash Sutcliffe coefficient (NSE). The results indicate that there is no significant difference between the performance of the model in predicting the underground water level in annual and monthly periods. But the SVM-SA model with Nash Sutcliffe coefficient of 0.993, root mean standard error of 0.417 and explanatory coefficient of 0.993 in the calibration period has been more accurate in monthly estimation of underground water level. In the next step, in order to achieve the best SARIMA and ARIMA models for predicting monthly and annual rainfall values, statistical indicators of coefficient of explanation (R2), root mean square error (RMSE), mean standard error (SE) and goodness of fit (AIC) are used. Finally, by using the ranks of the selected models according to the evaluation indices for monthly and annual periods for Aghkahriz station, respectively SARIMA(3,0,1)*(1,0,1) and ARIMA(3,0,2), for Ekbatan dam station according to SARIMA(1,0,1)*(1,1,2) and ARIMA(3,1,3), for Kooshkabad station according to SARIMA(1,1,3)*(1,1,1) and ARIMA(2,0,3) and for Marianaj station by SARIMA(1,0,1)*(1,1,2) and ARIMA(3,0,2) respectively, rainfall values for 5 years in monthly and annually forecast it placed. Finally, using the forecasted rainfall values with the help of SARIMA and ARIMA models, the groundwater level was forecasted monthly and annually for the next 5 years using the SVM-SA model.

One of the important results of this study is the absence of a significant relationship between the decrease in rainfall and the sharp drop in groundwater in the Hamedan-Bahar plain. In fact, the results of this research indicate that the sharp drop in the underground water level is caused by the excessive extraction of these valuable resources.

Sugarcane is a plant that has the most water requirement in the summer when the least rainfall occurs, and there is a need to irrigate this plant. In this research, the simulation and modeling of sugarcane cultivation with a focus on the water-environment-food nexus, utilizing the system dynamics approach, have been conducted at the Hakim Farabi Khuzestan Agro-Industry Company. This research was modeled using Vensim software. The model is an integrated and interconnected simulation of water consumption, product production, drainage water volume, salinity, and soil salinity. The information of three years 2015 to 2017 was used for calibration and the information of two years 2018 to 2019 was used to validate the model. MAE, MBE, and MAPE statistical parameters were used to evaluate the model results. The modeling results showed that the model has high accuracy in the calibration period with an MAE index of 6.31 ton/ha for crop yield, 53.56 mm for water drainage volume, 1.21 dS/m for water drainage salinity, and 0.09 dS/m for soil salinity. Also, the results of the same index in the validation period, which were 3.04 ton/ha for crop yield, 48.76 mm for water drainage volume, 1.11 dS/m for water drainage salinity, and 0.04 dS/m for soil salinity, indicate that the model is highly accurate in simulating the existing conditions. The highest water productivity was achieved at a rate of 3.75 kg/m³ in 2019.

New Mobility Services (NMS) are more popular every year. Sharing economy solutions acquire the demand for travel services, especially for short distances. The evolution of business models on the mobility market has an impact on mobility choices of all urban residents, especially the younger ones, described as the Y generation. Therefore, the study aims at indicating the level of popularity of Mobility-as-a-Service (MaaS) and differences between the subcohorts of the Y generation in Poland.

In recent years, the conflict between environmental interests and farmers' livelihoods has emerged as one of the most crucial issues in the environmental and agricultural governance of the Zayandeh-Rood watershed. Hence, achieving a balance between economic profitability through the use of an optimal cropping pattern and preventing the excessive extraction of groundwater resources holds great importance for policymakers in this field. In this study, the environmental function was modeled by considering water resources limitation, while the economic function was modeled based on the economic profit derived from the cultivated crops in Kohpayeh-Segzi Plain in 1996-2011. The Nash function was optimized by using constrained nonlinear optimization by taking into account the limitation of water resources in each year. After optimizing the Nash function, the cropping pattern was determined using both game theory (GT) method and linear programming (LP) method. Results indicated 30% reduction in water consumption with the GT and 17% reduction with the LP method. Additionally, the profit reductions for these two methods were 16% and 3%, respectively, compared to the base case. Furthermore, the results revealed that the groundwater level in the representative hydrograph of the plain decreased by 3.94 and 2.23 m in the GT and LP methods, respectively. Conversely, in the optimized GT method with an economic function weight of 0.5, the groundwater level of the plain increased by 8.67 m. Considering the reduction of water consumption, profit reduction, and the increase in groundwater level, the optimized GT method with a weight of 0.5 was superior to the LP method.

The basic purpose of this study is to investigate and display causal relationships among collaboration components in supply chain 4.0 using a fuzzy framework. The power of collaboration increases with the effect of Industry 4.0 technologies for the improvement of supply chain performance, so supply chain 4.0 is the context of this study. To achieve the research purpose, after reviewing articles and extracting indicators, a collaboration model with trust, initiators, barriers, dimensions, and outcomes was designed. Then using the fuzzy DEMATEL method, the effect of each variable and its position were determined. To collect data targeted sampling and snowball methods were used. 20 questionnaires were distributed to supply chain and digital technologies experts. Findings show that Trust and Information and Communication Technology infrastructure are closely related and are considered the most fundamental factors of the collaboration conceptual model, and can lead to more serious and effective changes in SC 4.0 such as improved Economic and Social performance. SC 4.0 managers can facilitate the development of collaborative trust across the SC By investing in communication and technology infrastructure.

In order to control and minimize the damaging impacts of floods, flood modeling or simulation is a fundamental solution. Identifying effective models for this purpose is crucial in watershed management. This study evaluates the accuracy of support vector machine models combined with the support vector machine (SVM), Grasshopper algorithm (SVM-GOA) and least square support vector machine (LS-SVM) in simulating the flood peak discharge of Poldokhtar station in the Karkheh basin. For this study, 74 flood events from 2009 to 2016 at the Poldokhtar station and data from 13 daily rainfall stations in the upstream area for the same period were utilized. Subsequently, 52 events were allocated for training, and 22 for validation. The comparison of results was conducted using three statistical indicators: Correlation coefficient (R2), Root mean square error (RMSE), Nash efficiency (Ns), and Standard error (SE). Additionally, uncertainty analysis was performed using two indexes: ARIL and POC. The results indicate the relative superiority of the LS-SVM model with SE=0.407, RMSE=110.16, NS= 0.91 and R2=0.92 compared to the SVM model with SE=0.5, RMSE=137.70, NS= 0.87 and R2=0.88 and SVM-GOA model with SE=0.519, RMSE=144.53, NS= 0.83  and R2=0.9. The study's overall conclusion is that the LS-SVM model is more accurate, faster, and easier to implement compared to the SVM and SVM-GOA models. As a result, it can be confidently preferred over the SVM and SVM-GOA models due to its significant advantages. The research emphasizes the critical importance of precise flood modeling and simulation in watershed management for mitigating the destructive impact of floods.

In this paper, the modeling of a make-to-order problem considering the order queue system under the robust fuzzy programming method is discussed. Considering the importance of timely delivery of ideal demand, a four-level model of suppliers, production centers, distribution centers, and customers has been designed to reduce total costs. Due to the uncertainty of transportation costs and ideal demand, the robust fuzzy programming method is used to control the model. The analysis of different sample problems with LCA, PSO, and SSA methods shows that with the increase in the uncertainty rate, the amount of ideal demand has increased and this has led to an increase in total costs. On the other hand, with the increase of the stability coefficients of the model, contrary to the reduction of the shortage costs, the total costs of the model have increased due to transportation. Also, the analysis showed that with the increase in the number of servers in the production and distribution centers, the average waiting time for customers' order queues has decreased. Because by reducing the waiting time, the total delivery time of customer demand decreases, and the amount of actual demand increases. On the other hand, due to the lack of significant difference between the OBF averages among the solution methods, they were prioritized and SSA was recognized as an efficient algorithm. By implementing the model in a real case study in Iran for electronic components, it was observed that 4 areas of the Tehran metropolis (8-18-16-22) were selected as actual distribution centers. Also, the costs of the whole model were investigated in the case study and the results show the high efficiency of the solution methods in solving the MTOSC problem.

This paper discusses the modeling of a location-routing-inventory problem for perishable products. The model presented in this paper includes a three-echelon supply chain of suppliers, distribution centers, and retailers. Supplier selection, assigning suppliers to distribution centers and retailers, vehicle routing and economic order quantity, lead time, and confidence inventory are the main decisions of the problem. These decisions are aimed at optimizing the total supply chain network costs. The nonlinear model presented in this article has been solved using two algorithms, WOA and ALO, in 12 sample problems. The results show that the solving speed of these algorithms and the high quality of the obtained answers are very high compared to the exact method. So, the maximum percentage of relative difference between the obtained results is less than 1%. The sensitivity analysis on the perishability rate also shows the increase in total costs in line with the increase in this parameter. By examining the outputs of 12 sample problems in large size, the WOA showed its efficiency compared to the ALO in terms of two indicators of average total costs and CPU time.

In a recent competitive and challenging market, supply chain management has faced many challenges due to rapid technological changes, new products and variable customer tastes. Therefore, supply chain management seems to require more vigilance and speed leading to the formation of the concept of the agile supply chain. Since supply chain management plays a significant role in food industries and due to the specific nature of the food companies as well as the importance of their supply chain agility, the main purpose of the current study is to evaluate and prioritize the success key factors for agile supply chains in food companies. In this regard, a D-ANP method is employed as a hybrid decision-making method considering the Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP). The results reveal that among 17 factors of success for agile supply chains in these companies, employee skill development, utilizing robust scheduling systems in distribution and process integration are the highest priority.

Innovation can be a source of competitive advantage for companies. Therefore, recognizing and evaluating innovation in manufacturing and service industries is an effective step to start measuring innovation in these industries. Considering the importance of this issue, it is very important to know key dimensions and indicators to measure these innovations. Hence, the purpose of this research is to address each of the main indicators of organizational innovation and discuss the inherent advantages and disadvantages of their deployment separately. In this research, based on the literature review and the opinions of 15 academic experts and manufacturing industry experts, 10 indicators have been categorized as the most important key indicators of innovation evaluation in three general categories of input, intermediary and output. In order to evaluate and prioritize the most important innovation evaluation indicators, a non-linear quantitative analysis based on hierarchical analysis (Mikhailov's method) was used in a fuzzy environment, and for the analysis of cause and effect relationships, the fuzzy Dimetal method was used. The results show that the presence knowledgeable people and the growth of education are the most important indicators in the evaluation of innovation, and therefore, with special emphasis and attention on these indicators, we can witness an increasing growth in the field of innovation in organizations. The amount of effort to register a patent as well as the number of registered patents are among other important indicators in the field of innovation evaluation, which should be given special support in order to develop innovation in organizations. Also, it was found that the protection of innovation during the growth period is the next priority and can be very important in creating a safe environment as well as increasing the peace of technology people in innovative organizations. The analysis of cause and effect relationships using the Dimtel method also showed that the development of knowledge capital and educated people has the highest impact on other factors, which confirms the hierarchical analysis.

This paper discusses the modeling and solution of a flexible flow shop scheduling problem with forward and reverse flow (FFSP-FR). The purpose of presenting this mathematical model is to achieve a suitable solution to reduce the completion time (Cmax) in forward flow (such as assembling parts to deliver jobs to the customer) and reverse flow (such as disassembling parts to reproduce parts). Other important decisions taken in this model are the optimal assignment of jobs to each machine in the forward and reverse flow and the sequence of processing jobs by each machine. Due to the uncertainty of the important parameters of the problem, the Fuzzy Jiménez method has been used. The results of the analysis with CPLEX solver show that with the increase in the uncertainty rate, due to the increase in the processing time, the Cmax in the forward and reverse flow has increased. GA, ICA and RDA algorithms have been used in the analysis of numerical examples with a larger size due to the inability of the CPLEX solver. These algorithms are highly efficient in achieving near-optimal solutions in a shorter time. Therefore, a suitable initial solution has been designed to solve the problem and the findings show that the ICA with an average of 273.37 has the best performance in achieving the near-optimal solution and the RDA with an average of 31.098 has performed the best in solving the problem. Also, the results of the T-Test statistical test with a confidence level of 95% show that there is no significant difference between the averages of the objective function index and the calculation time. As a result, the algorithms were prioritized using the TOPSIS method and the results showed that the RDA is the most efficient solution algorithm with a utility weight of 0.9959, and the GA and ICA are in the next ranks. Based on the findings, it can be said that industrial managers who have assembly and disassembly departments at the same time in their units can use the results of this research to minimize the maximum delivery time due to the reduction of costs and energy consumption, even though there are conditions of uncertainty

Prediction of evaporation as a key component of the hydrological cycle is one of the most important issues in water resources management and meteorology studies. In this study, the performance of ARIMA, SARIMA, gene expression programming, multiple linear regression, Monte Carlo and Thomas Fairing models in prediction of monthly evaporation values of Ekbatan Dam station, west of Iran in a 47 years period (1971-2017) were evaluated. For calibration of these models, 40 years data (1971-2010), and for validation, data from 2011-2017 (7-year) were used. The statistical metrics of the correlation coefficient, root mean square error, standard error, the Akaike information criterion, and NSE were selected for evaluation and comparison of models. The results showed that the SARIMA model has more accurate performance in predicting monthly evaporation. The GEP model, ARIMA, and MLR are ranked second to fourth. However, since the GEP model is easier to use than the SARIMA model and requires fewer variables than the SARIMA model, it shows promise to generate faster results, therefore, the GEP models can be the preferred option compared to others.

Value creating through technology learning in corporation level, has been very remarkable in recent years, because three important reasons of paying attention to wealth making subject, development of technological abilities and creating competitive advantage has been converted to the strategic topics of corporation. The aim of this research is to define and prioritize the factors affecting on technology learning levels in Isfahan state corporations level.

Statistical population, are 104 corporations which are active in industrial context that examine the quintet assumptions by accumulating needed data by the use of a questionnaire with standard components and reliability coefficient of 0.845.

Regarding the results, it is demonstrated that four studied level in a structural equations model, have been truly explained technological learning levels in Isfahan state corporations level, and each one has been affective straightly on mentioned process. Accordingly, the straightest effectiveness related to average level (0.78), basic level (0.67) and global level (0.65) are respectively in next degrees of importance and also each of these affective factors on these levels have been discussed.

Originality/Value:

 According to the results, there are recommendations for managers to improve the levels of technology learning at the enterprise level, and how to pay attention to the identified factors to learn.

Precipitation is one of the basic components of the water cycle and it is considered as one of the most important input components of the hydrological cycle. In the current research, the accuracy of the Simulated Annealing algorithm based on Support Vector Machine (SVM-SA) was evaluated in the simulation of precipitation changes. In order to verify the results, the precipitation data of the Gorgan synoptic station during the 40-years from 1971 to 2010 were used . Based on the results, using 5 non-precipitation meteorological parameters including cloud cover, maximum temperature, water vapor pressure, maximum relative humidity, and dew point, the values of RMSE, SE and R2 in the training section are equal to 6.02 mm, 0.01 and 0.999, and in the testing section 18.72 mm, 0.03 and 0.925 mm, were calculated respectively. The results showed that the SVM-SA can be highly accurate in simulating precipitation changes in the study area.

This paper examines the use of the Internet of Things (IoT) in the Food Supply Chain (FSC) and identifies the strengths and weaknesses of this system. Since this paper is a review study, the papers published from 2014 to June 2021 have been studied and 93 articles related to the field of IoT applications in the FSC have been reviewed. By reviewing the literature, six basic applications obtained for this type of network include transportation procurement, food production, resource/waste management, food safety improvement, food quality maintenance, and FSC transparency. Clustering is used to achieve these. Cluster analysis suggests that researchers should pay more attention to IoT applications for product quality and transparency throughout the supply chain, and consider IT-based systems seamlessly at each level of the supply chain.

Selecting the optimal stock portfolio is one of the most important issues in the field of financial research, which tries to choose the optimal combination of assets in order to create maximum utility for the investor, Given that the return on securities in the real world is often vague and inaccurate, one of the most important investment challenges is uncertainty about the future. In this paper the problem of selecting and optimizing securities portfolios with different modeling goals has been solved and compared. The designed models have considered both the nature of the portfolio selection issue and the considerations considered by the shareholder in the portfolio selection. The uncertainty quality of the future return of a given portfolio is estimated using fuzzy LR numbers, while its return torques are measured using possibility theory. The most important purpose of this paper is to solve the problem and compare portfolio selection models with simultaneous optimization of two, three, and four objectives. For this purpose, the NSGA-II genetic algorithm is used and the mutation and intersection operators are designed specifically to generate possible solutions to the cardinality constraint of the problem. Finally, the efficiency and performance of the models in case of using fuzzy logic and not using it have been compared and it has been determined that the use of fuzzy logic and possibility theory leads to the formation of portfolios with higher performance and higher efficiency.

In recent years, the high complexity of the business environment, dynamism and environmental change, uncertainty and concepts such as globalization and increasing competition of organizations in the national and international arena have caused many changes in the equations governing the supply chain. In this case, supply chain organizations must always be prepared for a variety of challenges and dynamic environmental changes. One of the effective solutions to face these challenges is to create a resilient supply chain. Resilient supply chain is able to overcome uncertainties and disruptions in the business environment. The competitive advantage of this supply chain does not depend only on low costs, high quality, reduced latency and high level of service. Rather, it has the ability of the chain to avoid catastrophes and overcome critical situations, and this is the resilience of the supply chain. AI and IoT technologies and their combination, called AIoT, have played a key role in improving supply chain performance in recent years and can therefore increase supply chain resilience. For this reason, in this study, an attempt was made to better understand the impact of these technologies on equity by examining the dimensions and components of the Artificial Intelligence of Things (AIoT)-based supply chain. Finally, using nonlinear fuzzy decision making method, the most important components of the impact on the resilient smart supply chain are determined. Understanding this assessment can help empower the smart supply chain.

In this research, the problem of combined transportation in the supply chain of fruits and vegetables under uncertainty has been modeled. The designed model includes 4 levels consisting of cultivation, packaging, distribution and customer centers that aim to meet customer demand for perishable products (fruits and vegetables) under conditions of uncertainty in different scenarios. The presence of multiple vehicles in the supply chain network at different costs has led to the model showing the most suitable combined transport based on the results of the model solution by CPLEX method. Data, and as the probability increases or decreases, the amount of transfer time decreases. The result of changes in uncertainty rates also shows that with increasing uncertainty rates, the amount of demand increases and as a result, more transportation options are used for transportation. This has led to an increase in product transfer time. In the most important sensitivity analysis regarding the time of corruption, it was found that with the increase of corruption time, due to the possibility of storing perishable products and avoiding unwanted transportation, the possibility of using high speed vehicles has been provided and transfer time has decreased. Also, by analyzing the objective function and computational time in larger sizes with SCA and GA algorithms, it was observed that there is no significant difference between the mean indices and the SCA algorithm has a higher efficiency than the GA algorithm in obtaining the value of the objective function in acceptable time.

In addition to playing an important role in creating economic security and investment development, insurance companies also invest. The country's insurance industry as one of the country's financial institutions has a special place in the investment process and special attention to appropriate investment policies in the field of insurance industry is essential. So that the efficiency of this industry in allocating the existing budget stimulates other economic sectors. This study seeks to model investment in the performance of dynamic networks of insurance companies.

In this paper, a new investment model is designed to examine the dynamic network performance of insurance companies in Iran. The designed model is implemented using GAMS software and the outputs of the model are analyzed based on regression method. The required information has been collected based on the statistics of insurance companies in Iran between 2012 and 2019.

After evaluating these units, out of 15 companies evaluated, 6 companies had unit performance and were introduced as efficient companies. The average efficiency of insurance companies is 0.78 and the standard deviation is 0.2. The results show that the increase in the value of investments is due to the large reduction in costs and in terms of capital and net profit of companies is a large number that has a clear and strong potential for insurance companies.

Originality/Value:

 In this paper, investment modeling is performed to examine the performance of dynamic networks of insurance companies in Iran.

Today, due to industrial development in the world, the variety of products has increased and products have special complexities. Lean supply chain is an approach aimed at producing and delivering products in the fastest possible time with the least production waste. The lean supply chain approach is one of the most important strategies to help managers in the organization due to the nature of its activities and the volume and variety of products, suppliers, and customers within the organization, with very diverse needs and very high geographical dispersion. This approach can, as an effective tool, play a very functional role in reducing waste from the supply chain and reducing organizational costs. Today, evolving technologies such as the Internet of Things and blockchain play a significant role in facilitating lean supply chain creation. The Internet of Things (IoT), along with blockchain technology, provides instant insight into every move of the goods made in the supply chain and more responsibility than ever before. In addition, IoT eliminates many of the paperwork requirements prone to supply chain management error and simplifies processes for less efficient product management from warehouse to final destination. In this study, a framework for a lean supply chain based on these technologies was first proposed. Then, the critical success factors in this lean supply chain were extracted using the literature and expert opinions. In order to evaluate these factors and study their internal relationships, a nonlinear fuzzy approach and fuzzy DEMATEL method were used. The results show that quick response to customer needs is one of the most important critical factors for the success of the lean IoT- blockchain based supply chain.

Over the past thirty years, logistics has undergone tremendous change from a purely operational performance that led to sales or production and focused on securing supply and delivery lines to customers, with the transformation of intelligent technologies into a professional operator. In today's world, the fourth generation of industry has forced companies to rethink how their supply chain is designed. In this case, in addition to the need for adaptation, supply chains also have the opportunity to reach operational horizons, use emerging digital supply chain business models, and transform the company into a digital supply chain. One of the transformational technologies that has had a tremendous impact on the supply chain in this regard is IoT technology. This technology, as one of the largest sources of data production, can facilitate supply chain processes in all its dimensions. However, due to the presence of the Internet and the location of supply chain components in the context of information networks, this digital supply chain always faces major challenges. Therefore, in this paper, an attempt was made to examine and prioritize the most important challenges of implementing a supply chain 0/4 using a nonlinear hierarchical analysis method. In order to investigate these challenges, the opinions of experts active in the supply chain of Fast-moving consumer goods industries (FMCG) were used as a case study as well as some academic experts.The results show that the lack of technological infrastructure and security challenges are the most important challenges of implementing supply chain 0/4 in the era of digital developments, which should be given special attention for a successful implementation.

We present a mathematical model for the problem of collaborative production system based on order with fairness to allocate production loads. The main objectives of the model are to minimize total production costs and maximize the use of resources in order to distribute production loads fairly in conditions of uncertainty. Fuzzy programming was used to control uncertain parameters. The results show that, with increasing the uncertainty rates, production system costs have increased. Since the capacity of factories is constant, with the increase in demand, the amount of production has increased and the maximum use of resources of each factory has also increased. Also, contrary to the trend of system cost changes, with the increase in the number of factories, the maximum use of available resources has decreased. To solve large sample problems, the NSGA II algorithm with a suitable chromosome is used to search the problem space. Numerical results of solving 15 sample problems show the high efficiency of NSGA II algorithm in solving the problem of cooperative production system in a very short time.