hassan javanshir

Taking advantage of short-term traffic forecasts and using it in traffic management will reduce possible blockages and by reducing the travel time and distance traveled by cars, it will reduce noise pollution, air pollution and also consumption costs. It will be fueled. By knowing the traffic in different hours and predicting it, you can have a better management and planning for the roads of the country, the characteristics of the traffic flow in a road is one of the most important factors in decision making and traffic policy in a region. In this study, using traffic evaluation protocols by means of neural network, a traffic evaluation prediction model was built. Also, the technical evaluation and its economic benefits are discussed. In this regard, by using the traffic count in urban highways and by using the neural network model, the traffic prediction has been done. In the neural network, 4 input variables were used, low or high density, The volume of the number of cars, the type of vehicle, the flow speed, the number of 10 neurons were used in the hidden layer, and finally the numerical model was displayed as a numerical matrix. To predict the current density with proper accuracy, which was done here with r=0.93. Also, to compare the linear regression model, the accuracy of the regression model is lower than the neural network model and r=0.88 was obtained at a significance level of less than 0.05.

Today, due to financial crises and successive droughts, collecting and distributing food with high nutritional value and freshness for the homeless and low-income people has become a global issue. Therefore, most countries are trying to manage this issue by creating centers called food banks. The importance of the food bank issue has led to the modeling of the food bank network issue based on IoT and Big Data in this article. The problem of the studied food bank network consists of a set of donors, food banks and charities. In this network, donors provide food needed by charities for free. Food banks also distribute food collected from donors to charities. The purpose of presenting this model is to reduce the costs of the entire food bank network along with reducing the maximum unmet demand. The results of the model analysis on a case study in Iran show that with the reduction of the shortage in the network, the total costs increase. So that the results of the mathematical model in a case study in Tehran metropolis showed that three areas 11, 15 and 19 were selected as food banks and 12 vehicles are needed. In this study, 10 different efficient solutions were obtained with LP-Metrics and 11 different efficient solutions were obtained with Sum weight method (SWM). Also, if the amount of demand increases, in addition to increasing the shortage in the network, the costs related to transportation and providing food needed by charities have also increased.

BOT projects for the construction of suburban pedestrian bridges are very important as one of the appropriate methods for transportation infrastructure development in cities. Considering the economic and financial effects on these projects, it is vital to know and analyze the factors affecting the pricing of these projects. The purpose of this article is to identify and rank the factors affecting the pricing of BOT projects for the construction of suburban pedestrian bridges.

The research approach is quantitative and practical. The statistical population includes the managers and experts of the Road Maintenance & Transportation Organization and university professors specializing in this field. Fuzzy Delphi method was used to identify factors and BWM was used to prioritize them.

By reviewing the literature, 40 factors were extracted from previous studies, and based on the fuzzy Delphi results, after three rounds of expert opinion polls, 31 factors affecting the pricing of BOT projects for the construction of suburban pedestrian bridges were identified and classified into five groups, then using BWM are rated in overall and their group.

The overall ranking showed that the most important factors are the costs of pedestrian bridge construction, maintenance and operation, and equipment and personnel. The ranking in groups also showed that the main factors were ranked as the factors related to costs, contract setting, project, government, and the private sector. These results can help decision makers in this field to make the best decisions in the pricing process of similar projects.

The process of transferring money from the treasury to the branches and returning it at specific and limited periods is one of the applications of the Vehicle Routing Problem (VRP). Many parameters affect it, but choosing the right route is the key parameter so that the money delivery process is carried out in a specific period with the least risk. In the present paper, new relationships are defined in the form of three concepts in order to minimize route risk. These concepts are: 1) the vehicle does not travel long routes in the first three movements, 2) a branch is not served at the same hours on two consecutive days, and 3) an arc should not be repeated on two consecutive days. The proposed model with real information received from Bank Shahr has been performed for all branches in Tehran. Because the  VRP is an NP-Hard problem, a genetic algorithm was used to solve the problem. Different issues in various production dimensions were solved with GAMS and MATLAB software to show the algorithm solution quality. The results show that the difference between the genetic algorithm and the optimal solution is an average of 1.09% and a maximum of 1.75%.

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One of the main processes in the banking system is the planning and transfer of money from the treasury to the branch and its return to the treasury within a specific and limited time frame. Accordingly, the main goal of most banks is to minimize route risk. Because, daily, a large amount of cash is moved by cash vehicles. In this research, a mathematical model for the transportation of physical money has been developed considering the route risk. In the proposed model, three concepts are presented, which are: 1) the vehicle should not travel long distances in the first three trips because it carries more money, 2) a branch should not be serviced on two consecutive days at the same time 3) A route should not be repeated in two consecutive days. This reduces the possibility of determining a fixed pattern for servicing and increases the security of servicing. Also, a genetic meta-heuristic algorithm has been used to solve the model. In order to show the quality of the algorithm's answer, various problems have been solved in various dimensions of production and with GAMS and MATLAB software. The results show that the genetic algorithm has an average of 0.93% and a maximum of 1.87% difference with the optimal solution in these problems.

The purpose of this research is to model the supply chain in the conditions of uncertainty from the point of view of Jihadi management. In this research, the modeling of the parameter after the crisis has been done.The importance and position of Jihadi management's view regarding critical conditions is one of the things that was considered in this research. In order to identify the weaknesses and bugs of the existence, from the point of view of the elite group with jihadi management in the field of cement industries, interview tools and content analysis technique for 7 people has been used.For the implementation of the model in larger dimensions, genetic algorithms, particle swarm optimization and simulation cooling were used, and according to the results of the implementation of the algorithms for different dimensions, the genetic algorithm has a higher quality.The reason for examining the role of jihadist management in this process is that in the conditions of uncertainty, the existence of a jihadist thought leads to effective decisions.Since Jihadi management perspective has been used in this research, this model can be designed and implemented in various industries where the time factor is important, and the appropriate supply chain in critical situations.

 Air pollution is one of the major challenges of urban management in Tehran. Nowadays, the problem of air pollution and traffic congestion in the metropolis of Tehran, like most major cities in the world, is quite concerning. Land use and transportation are two main sectors that have a maximum contribution to environmental pollution.

The impetus of this article is to investigate the urban land use efficiency, the distribution of land use, and their impact on transportation and pollutants. For this purpose, the following question was raised: What has been the impact of urban land-use efficiency and distribution of land use in Tehran on transportation and air pollution over the last two decades?

The present paper is a real-life case study for which data was collected through documentary-library, municipal data, and satellite images. In this study, Landsat satellite images, ENVI 5.3, ArcGIS 10.8, Google Earth Pro, and SPSS 24 were used.

The results of this study indicate the expansion of Tehran, the loss of vegetation,and their transformation into built lands. Over the past two decades, the growth of the built-up area in Tehranhas exceeded the population growth which indicates the inefficiency of the land during this period. In other words, the expansion of the city has been more towards the suburbs with low population density. The study of the correlation between land use types during the period 2004-2016 indicates the scattered growth of Tehran. In such situations, residents use more private cars, which increases traffic congestion, fuel consumption, and air pollution.

Transportation and land use are the two major sectors that contribute the most to the spread of environmental pollution. Transportation through accessbility affects land use while land use through the production and attraction of trip affects transportation. The purpose of this study is to investigate the accessibility of 22 districts of Tehran metropolis, how the city develops and their impact on increasing air pollution. This is an applied research where the data and information used in this research were obtained from Tehran Municipality and Statistics Center of Iran and for their classification and analysis, depending on the need, SPSS, Arc GIS and Google Earth software have been used. The results of Pearson correlation show that about 24% of land uses in Tehran have a significant relationship, which indicates a high separation between types of land uses and ultimately the scattered growth of Tehran. By analyzing and calculating the accessbilities, it was found that 38% of the internal communications between the districts are at the level of good and very good accessbility, 32% have the average level of accessbility and 30% are at the level of poor and very poor. The average route factor of Tehran is estimated to be 1.52, which indicates that Tehran is classfied in the average level of accessbility. Another finding of this study is the effect of land uses and accessbility levels on increasing air pollution in Tehran which is important to urban planners and policy makers.

This study deals with a bi-objective flexible flow shop problem (BOFFSP) with transportation times and preventive maintenance (PM) on transporters via considering limited buffers. The PM actions on transporters are a missing part of the literature of the flexible flow shop problem (FFSP) which before the breakdown occurs, each transporter at each stage is stopped and the PM action is performed on it. The capacity of each intermediate buffer is limited and each job has to wait in the intermediate buffers. By including all these features in the proposed BOFFSP, not only processing times affect the objective functions, but also, the transportation times of jobs, the waiting time of jobs in the intermediate buffers, and availability of transporters in the system are considered in the model and make it a sample of a real-world FFSP. The presented BOFFSP has simultaneously minimized the total completion time and the unavailability of the system. As the problem is NP-hard, a non-dominated sorting genetic algorithm II (NSGA-II) and a multi-objective particle swarm optimization (MOPSO) is proposed to solve the model for large size problems. The experimental results show that the proposed MOPSO relatively outperforms the presented NSGA-II in terms of five different metrics considered to compare their performance. Afterwards, two one-way ANOVA tests are performed. It can be observed MOPSO achieves relatively better results than NSGA-II. Finally, sensitivity analysis is conducted to investigate the sensitivity of the objective functions to the number of jobs and their transportation time at each stage.

These days, emergency request responding in a short time is a highly challenging issue. The main goal of current research is modeling an optimization problem for emergency vehicle routing on small and medium-sized networks and solving the problem by a novice algorithm. The suggested algorithm inputs are the network, location and number of requests, vehicles, and hospitals. The objective is to minimize total travel time of vehicles plus the time that the last request is responded completely. The algorithm output is the vehicle and hospital assignment to each request and requests' responding order on the network. After developing the mathematical model of the problem, 138 random samples are produced on a network with 7 nodes and 16 links. The number of feasible answers, which is depended on the number of requests, vehicles, and hospitals, is tried to be reduced by assuming a fixed order for responding the requests. Hence, 3 different scenarios of request order are examined and it is found out that arranging the requests based on distance from the nearest vehicle results in better answers (lower total travel times). An innovative algorithm is presented for solving the problem which reduces the solving time to 5 seconds; while finding the precise answer needs time up to 810 seconds. The results show that the difference between more than 80 percent of answers found with the suggested algorithm and the precise answer is lower than 10 percent and this difference is less than 30 percent in more than 94 percent of the answers.

For this purpose, in this study, modeling is designed to design a dynamic cement supply chain network to reduce post-crisis shortage. The facilities used in the model of this study are of the two types of temporary and permanent ones that are used to meet the severe demand created after the crisis. Time parameter One of the most important parameters is due to the cavitation of cement products and also the need to meet demand at specified time. In this research, this parameter is modeled in normal and post crisis situations. Disaster victims have a different preference for meeting demand. In addition to modeling this issue, alternative or similar items are also considered. Sustainable planning has been used to make sound decisions because of the demand-side sensitivity. Genetic algorithms, particle swarm optimization, and simulation refrigeration have been used to solve larger-scale models of the algorithm. According to the results, the algorithm is of better quality than the algorithms for different dimensions. Due to the importance of demand forecasting during the planning period in this study, the adaptive fuzzy neural network method is used for forecasting demand which FCM is the best known pattern recognition and adaptability algorithm.

Minimize supply chain costs as one of the essential issues in support activities such as financial planning systems,How to manage supply chain A set of ways to integrate Effective suppliers, manufacturers, warehouses and stores used to minimize total supply chain costs and meet customer service needs with a high level of service. In this study, the design of a robust cement supply chain dynamic network model was designed to reduce supply chain management costs after a crisis. Principal and efficient design of cement grid infrastructures, given the strong demand fluctuations at different times of the year, can significantly reduce financial costs on the one hand and reduce the potential for high-speed, high-cost corruption by correct prediction. Other leads.From the following tool The nose has been analyzed using artificial neural networks. The purpose of this study is to use artificial intelligence methodologies such as Grid Clustering, Subtractive Partitioning, FCM to explore fundamental and technical patterns and relationships in historical data. Used. To this end, a genetically-based inference fuzzy multilayer fuzzy neural network is introduced to prevent technical and economic unpredictability. The basic model of this paper presented by the researcher is a robust and multi-periodic planning for multi-product state under uncertainty.

In this study, to select suppliers of raw materials in Saipa Automotive Corporation as one of the largest factories in Iran, environmental criteria, flexibility and agility criteria are considered and some sub-criteria are also considered for each criterion. The sub-criteria include green design, clean technology, environmental performance, agility in operational systems, market agility, logistics agility, product flexibility, flexibility in transportation, resource flexibility. It should be mentioned that the said criteria may need modification and revision due to opinions of experts during the research implementation. Therefore, main variables for the issue of identification of criteria affecting selection of suppliers are studied with regard to environmental factors within the organization. In order to rank suppliers and to select the best option based on a best-worst multi-criteria decision-making method (BWM) and VIKOR-based approach was used. According to the calculations based on the proposed process in this study and the information about the desired criteria, 7 suppliers were selected as the best options. As the approach presented in this study has combined two worst-best method to determine weights and VIKOR method for final ranking of options, this approach can be also used in other studies.

Previous studies has shown that reinforced self-compacting concrete with fiber is more suitable for structural performance. The purpose of this study was to evaluate the effects of Metkaolin and alkali resistant glass fibers on the performance of self-compacting concrete. In this study, the rheological properties (L-Box, Slump flow, T50) and mechanical properties (compressive strength, tensile and flexural strength), and durability (chloride ion penetration and water absorption) have been investigated. The results of the experiments showed that by increasing the amount of fiber, the concrete performance decreases. It was also found that the glass fiber had no positive effect on the compressive strength of the concrete. The tensile and flexural strength of self-compacting reinforced concrete increases with increasing fiber content. Durability studies have shown that the presence of glass fiber initially increases the resistance to chloride ion penetration and then reduces it, and also increases the gradual absorption of concrete water. The results of the experiments showed that the simultaneous presence of Metkaolin and glass fibers with optimal percentages can significantly improve the mechanical properties and durability of self-compacting concrete.

The high rate of road accidents and fatalities caused by these accidents and also the role of speed as a factor in Occurrence of these incidents, Cause decision-makers of safety of road transportation issue to pay more attention to use speed cameras in rural roads. On the other hand, Due to limited financial resources and budget As well as the high costs of installation and maintenance of speed cameras, Prioritization of Equipping rural Roads with speed cameras, is very important. So this study is conducted aims to provide Prioritization Model of Equipping rural Roads with speed cameras based on network analysis process (ANP). Vector of important coefficient obtained from matrix of Paired comparisons show that the important coefficient of “the annual number of Accidents caused by speed factor per one kilometer” criterion is 0.29572. So this is the most effective criterion on decision on the subject of this study. Also after modeling, Rural Roads of Isfahan Province were selected as alternatives of decision and the output of Super Decisions, Prioritize the alternatives. The results show that Azadegan highway is priority number one, Isfahan-Shahreza-Abadeh road is priority number two and Isfahan-Ziar road is priority number three and these alternatives are preferred with a significant difference compared to other alternatives.

This paper considers a vehicle routing problem (VRP) for fuel transportation with time windows and environmental factors. In the presented model, in addition to minimizing the cost and risk reduction, balancing the path of the vehicle is taking into account with environmental impacts in order to find the safest path for the vehicle considering the economic aspects. The selected routes for the vehicles should be determined to do not exceed the certain amount of environmental air pollution. In the previous studies related to the vehicle routing for fuel transportation, there was no any study considering these three objectives with regard to environmental consequences. Due to such an NP-hard problem to solve large-scale problem, two meta-heuristic algorithms, namely NSGA-II and MOVDO, are proposed and the Taguchi method is used to tune the related parameters. The five indices are used to compare the performance of the foregoing algorithms. Finally, to assess the applicability of the presented model, 15 problems with different sizes are considered. Finally, the results obtained by each of these two algorithms are explained. The results obtained by solving the problems with different sizes show the appropriate efficiency and effectiveness in solving the large-sized problems in a short computational time.

Increasing pressures caused by negative consequences of transportation and traffic problems in major cities, have resulted many attempts towards improvement of planning and management of transportation systems, according to sustainability objectives. In this paper, a comprehensive model is developed using a system dynamics approach to evaluate sustainable urban transportation. This model includes social, economical, environmental, and urban transportation variables. Then, the validation of the model according to actual data for years 1994 to 2009 of City of Mashhad are performed. The base year for simulation was 2009 and the horizon year was 2044. This stage is very important because all of the analyses and decisions made in the following steps will be based on the calibrated model. The sensitivity analysis of the parameters of the model showed that the selected variables have considerable influences on urban transportation sustainability. The proposed model can be used to find the optimal strategy for sustainable urban transportation.