فهرست مطالب hamed nozari

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 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 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 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.

IoT technology is one of long waves of technology that will act as a platform for countries to rise or fall in the economic path. If countries and companies that take advantage of the opportunities ahead of this technology, they can join the ranks of global leaders and consolidate their position for decades. In recent years, Iran has carried out extensive activities to develop various technologies, but due to the multiplicity of technology fields and the spread of the country's potential in various fields, sufficient accumulation of knowledge and capital in one sector has not taken place. In general, in order for economic coexistence, there must be leaps in several industrial and technological sectors so that a country can become the backbone of a developed country. But since IoT technology is a common technology and is used in all sectors, there is a risk of spreading the scientific and capital potential of the country in its various fields. Therefore, it is necessary to create accumulation of knowledge, capital and wealth by focusing on priority areas. Due to this, in this research, the applied parts of IoT technology that can cause technological and economic leaps have been identified and then the importance and priority of these parts can be determined using nonlinear modeling approach. The results showed that focusing on energy intelligence in Iran has the highest advantage and focusing on this sector can ensure economic growth and technology of society.

Limited resources, including water, especially in our country is one of the challenges facing the agricultural sector. Due to the limitations of this vital resource, Iran's agricultural sector can control this crisis to some extent by properly managing the use of water resources, including increasing water use efficiency. In this research, water consumption efficiency and its effective factors in grape production in Bijar County have been estimated using data envelopment analysis method with input-oriented approach. The data of this study were obtained by collecting a questionnaire from 170 gardeners in this city during the 95-96 crop year. Also, the average results of estimating water use efficiency in CRS and VRS conditions were 51.94 and 83.59%, respectively. This difference is due to the inefficiency of the scale in grape production in this county. Therefore, with the increase of production scale in this region, the amount of water consumption efficiency will also increase significantly. The results of the study of factors affecting water use efficiency showed that the variables of education and history of grape production are significant and as education and history of grape production increase, water use inefficiency decreases. According to the research results, by educating and consulting with the help of sample producers, it is possible to increase water use efficiency in grapes production in Bijar County.

The worldwide prevalence of coronavirus disease (COVID-19) and the severe problems in the distribution of medical equipment have led to the modeling of multi-depot vehicle routing under uncertainty in the COVID-19 pandemic. The primary purpose of the proposed model is to locate warehouses and production centers and route vehicles for the distribution of medical goods to hospitals. A robust fuzzy method controls uncertain parameters, such as demand, transmission, and distribution costs. The effect of uncertainty using a neutrosophic fuzzy programming method shows that by increasing demand, the volume of medical goods exchanges and the number of vehicles used to distribute goods increase. This leads to an increase in the total cost of the problem and the amount of greenhouse gas (GHG) emissions. The results also show that using more vehicles reduces staff fatigue to distribute medical products and reduces the prevalence of the COVID-19 pandemic. In the most important sensitivity analysis of the problem on the capacity of the vehicle, it was determined that by increasing the capacity of the vehicle, fewer vehicles are used and as a result, the cost and amount of greenhouse gas emissions are reduced. On the other hand, this has led to a decrease in the prevalence of COVID-19 virus.

Examining the trend of the global economy shows that global trade is moving towards high-tech products. Given that these products generate very high added value, countries that can produce and export these products will have high growth in the industrial sector. The importance of investing in advanced technologies for economic and social growth and development is so great that it is mentioned as one of the strong levers to achieve development. It should be noted that the policy of developing advanced technologies requires consideration of various performance aspects, risks and future risks in the investment phase. Risk related to high-tech investment projects has a meaning other than financial concepts only. In recent years, researchers have focused on identifying, analyzing, and prioritizing risk. There are two important components in measuring investment risk in high-tech industries, which include identifying the characteristics and criteria for measuring system risk and how to measure them. This study tries to evaluate and rank the investment risks in advanced industries using fuzzy TOPSIS technique based on verbal variables.

Due to increasing gap between water supply and demand, paying close attention to the principles of optimal water allocation is inevitable. In the present investigation, allocations from Kalan Malayer Reservoir to demands identified by the Iranian Ministry of Energy were evaluated using MODSIM8.1. These demands are environmental and sustainability requirements, City of Malayer drinking water, agricultural requirement of the downstream lands, irrigation of Sahand Malayer farms and industrial centers. The analysis temporal scale was a 12-month period with monthly time steps during year 2016-2017. The purpose of this study was to evaluate the behavior of the dam reservoir when the inflow is reduced. Four scenarios including 5, 10, 15 and 20 percent river reduction in flow to the reservoir were investigated. The results indicated that with decrease in the river inflow, the stored water in the dam reservoir would decreased and there would be shortage to meet all demands. The deficiencies volume in supplying water to demands from the first scenario to the fourth scenario was 0.373, 0.748, 1.124 and 1.508 MCM, respectively. In other words, the relationship between the decrease in stored water volume in the dam reservoir and river inflow reduction was linear. Therefore, in order to meet the demands in all four scenarios, the allocation to the demand points should be reduced according to the priorities of the demands. The other solution is to reduce the target storage volume equal to the total deficiency volume in that month.

In recent years, with the growth of sustainable development as well as the growth of textual tools on information technology, supply chain agility has become very important as a powerful tool to compete in the business environment. In such an environment, supply chain stakeholders expect digital developments and efficient tools to play a role in these supply chain developments; Therefore, this tool, namely the Internet, can play a key role in agility and accelerate activity in the supply chain and create value in the organization. Given the importance of the supply chain and its prominent role as a core in industrial activities, the purpose of this study is to provide a framework for implementing an agile supply chain based on big data from the Internet of Things.

This research is of developmental and applied type and has been done qualitatively. In this study, the components affecting supply chain agility are identified based on big data from the Internet of Things and the conceptual framework of agile supply chain is presented.

The results of the research provide a framework that intelligently identifies the agility needs of the organization and strives to achieve it, thus creating a more competitive advantage for the company and increasing customer satisfaction and expanding the market share of the organization.

This framework can be an effective guide to implementing an agile and clean supply chain based on the use of big data.

In urban water distribution networks, as the age of the pipes increased, the rates of pipe breakage and water losses increased which causes financial losses to the water and sewerage company. Therefore, to reduce the number of breaks and leaks in pipes, worn pipes should be replaced. Replacing old pipes is an expensive and costly procedure. Thus, it may be more economical to replace the pipe at the appropriate time. In this research, the urban water supply network of Hamedan was simulated by using system dynamics approach. Then the optimal time for pipe replacement was estimated by using analytical solution of the net present value equation and the combined method of system dynamics-net present value. Also, in order to improve the financial balance of Hamadan Water and Sewerage Company, the optimal price of drinking water was determined by using the Powell optimization algorithm method. Dynamic simulation was performed in VENSIM. To test the model, the simulated data were compared with the observed data collected from the water and sewerage organization of Hamedan over the period 2014 to 2018. To statistical analysis and calculating statistical, the root mean square error, standard error and correlation coefficient (R2) were used. Base on the results, for the Benefit-Cost variable, the RMSE index was calculated to be 0.006, the SE to be 0.010 and the R2 to be 0.991. Comparison of the standard error showed that the difference between the observed and simulated values of the standard error of the Benefit-Cost variable is less than about one percent. The result indicate that the presented model has the very high accuracy. Base on the results of the study, the optimal time for pipe replacement is 2041. Finally, the results of this study show that the financial balance of the company can be increased by determining the optimal price of drinking water. However, the level of social welfare can be improved by increasing the renewal coefficient of pipes. Therefore, the system dynamics method and Powell algorithm can be used to determine the optimal scheduling of water pipe replacement, to reduce losses to water and sewage companies and to reduce the number of failure of water supply pipes.

The Internet of Things (IoT) is a system of computers, computing machines, digital and mechanical devices, humans and animals, each with an independent code and as a distinct device, in the network and a significant share of information exchange and behavior and the relationship between them is done without direct human intervention and supervision. Businesses are no exception and try to use the IoT to make business smarter. The supply chain is one of the most vital parts of the business cycle and its smartening creates a dramatic change in the optimization of business processes. However, since this technology is based on the Internet and the use of the Internet can always be associated with different risks, so recognizing the cyber risks that can be faced by businesses in the face of these technologies is always significant IoT-based supply chain cyber to be identified and analyzed. In order to rank these risks, nonlinear mathematical modeling method has been used. The results show that privacy in interaction with suppliers as well as customers is the most important and effective measures should be taken to address these risks.