حسن جوانشیر

The increase in motor vehicles has greatly increased air pollution in the Tehran metropolis and turned it into a challenge. By analyzing transportation pollutants, low-carbon cities and sustainable development strategies for urban districts can be achieved. This study deals with the spatiotemporal analysis of pollutants and transportation accessibility in the urban districts of Tehran. The concentrations of CO and NO2 pollutants were extracted from the Sentinel-5P satellite using coding in the GEE environment. OSM data were used to calculate the accessibility of the 22 urban districts. Moran's global autocorrelation indicated a clustered spatial distribution pattern of pollutants. The high autocorrelation value equal to 0.94 for CO and NO2 pollutants indicated their cumulative state in the atmosphere. By performing LISA, most clusters of these pollutants were identified in the central districts and southern suburbs. The average accessibility indicator of the 22 districts was 24.82 km/km2. About half of Tehran's urban districts had a high density of road transport networks. These districts are located in CO and NO2 clusters owing to the high traffic volume of motor vehicles. The results of this study emphasize the factors that aggravate Tehran's air pollution, including the clustered pattern of pollutants in the atmosphere and dense transportation network in urban districts. By integrating air pollution analysis and accessibility estimations into urban planning frameworks, cities can strive to achieve long-term sustainability goals.

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 economical 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.

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.

The purpose of this research is to model the behavior of the government and the contractor in the early termination of BOT projects for the construction of suburban pedestrian bridges, using game theory. The case study includes BOT projects for the construction of pedestrian bridges in the Tehran-Semnan highway. By applying game theory and analyzing the behavior of the parties in the form of a bargaining game, the consequences of the decisions of the government and the contractor in the conditions of early termination of projects have been investigated and a model has been presented to predict these behaviors and their economic results. The data used in this research were extracted from the experts of the General Department of Highways and Road Transport of Tehran and the managers of BOT projects, as well as related documents. The proposed model provides three key features for the analysis of parties' behaviors, and using a practical example, these features have been calculated and verified. The results show that the amount of compensation and the decisions around it are directly related to the behavior of the government and the contractor; High compensation could lead to a government counteroffer or construction of a new bridge, but may delay negotiations, while low compensation would cause the project to continue inefficiently and harm the public interest. Finally, managed anticipated behaviors in the public and private sector and reaching an agreement on compensation is suggested as a suitable solution to reduce behavioral conflicts and maintain public interests.

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.

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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.

Today, smart buildings, which combine sensors and actuators, allow owners to save energy, increase security, provide users with information about their work environment, and operate directly on the building via the Internet. Indoor environmental parameters such as temperature, humidity, lighting, and air quality are monitored by IoT sensors in smart buildings. In this paper, we propose a smart building solution for managing the indoor environment based on the Internet of Things, which aims to provide functions such as monitoring the environmental parameters of the room and detecting the number of users in the space. It is also a cloud platform where virtual entities collect data obtained by sensors and cloud virtual entities perform data analysis tasks using system learning algorithms. This cloud platform also includes a control dashboard for managing and controlling the building. The goal of this article is to define, develop, and present the results of a project that uses sensors in a standard business center to monitor and condition business units using Internet of Things technologies. The arrangement and location of the sensors are explained in this study, followed by the sensors used, wireless interfaces, and cloud technologies. The project results are presented using Bluemix and Node Red.

 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.

The aim of this study was to design a new fuzzy expert system for project portfolio risk management in ongoing projects of a first mobile holding.

To do this; To identify the risks from the Dematel method developed in Excel, to evaluate and determine the most important risks from the fuzzy FMEA methods in Excel and the AHP method in Expert Choice software; And to select the optimal risk response, a proposed mathematical model is used and solved in Gamz software.

The research findings showed that there are 23 risks in the studied projects, 8 of which were of high importance and impact. Also, risk prioritization and determination of their impact level was performed using the integrated AHP-FMEA method. Findings obtained from solving and implementing the mathematical model in Gamz software also showed that the proposed model of efficiency and capability for selecting optimal risk response strategies is established.

Originality/Value:

The general findings indicate that the proposed new fuzzy expert system also has the necessary capability and efficiency to manage the portfolio risk in the study holding.

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.

In the past years and with the start of Autumn and Winter seasons in Tehran, when vehicle emission rates reached their maximum amount in Tehran and According to the approved plan of the emergency committee for air pollution in Tehran, traffic congestion zone was extended to the Odd-Even plan area and many of the citizens changed their way of access to the area and other citizens paid the tolls to travel inside the zone with their private vehicles. In this research, first, the interviewees were asked to determine how they have traveled to the Odd-Even plan area. In the following, the proposed hypothesis was based on this assumption that if the traffic congestion zone due to the air pollution problem, is extended to Odd-Even plan area, According to the different scenarios, which travelling means they choose. After reviewing the results obtained from the questionnaire and using the Aimsun simulation software, the amount of emissions produced by the movement of vehicles when implementing the Odd-Even plan area within their range and the above scenarios have been investigated and evaluated. the results of this study show that the most ideal mode for reduction of pollutant values in the Odd-Even plan area, was with an increase of 40-75 % of the money allocated for the traffic congestion zone in the year of 2016 and with extending of that in the Odd-Even plan area, The relative abundance of travelling with non - public transport means, caused to reduce 21-23% and in parallel of reduction of travelling with non - public transport means, the amount of produced cars emissions for the CO, HC and NOX pollutants has been decreased 39, 36 and 30 percent in relation to the usual mode of implementation of the Odd-Even plan within its range, it will be reduced.

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.

In Recent years, different Traffic schemes such as the Odd-Even day plan starting from the door of each house or the extension of traffic congestion zone to the Odd-Even plan range have been implemented when vehicle emission rates reached their maximum amount in Tehran. In this study, according to the approved plan of the Emergency Committee for air pollution in Tehran to extend traffic congestion zone to the Odd-Even plan range in Tehran in the third and fourth week of November 2016 and by using the information obtained from the amount of personal car usage of citizens in a selected area of Tehran, which includes 15 intersections and their traffic statistics harvested by the SCATS system, the volume of cars and their emissions rate in this zone during the above-mentioned period and also in the second week of November and the first week of December 2016, during which the Odd-Even plan was also in effect, was simulated and calculated with Aimsun Simulation software. In another stage of this research by using distributed The results shows that during the weeks that Tehran traffic congestion zone extended to the Odd-Even plan range, and Specifically the comparison for the first day of implementation of traffic congestion zone in the range of Odd-Even plan with the same day in the previous week (implementation of Odd-Even plan range) in this research, produced cars emissions for the CO, HC and NOx pollutants has been decreased 20, 18 and 14 percent.

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.

Iran is one of the countries where there are a lot of traffic accidents and the number of people killed and injured in road accidents is high compared to other countries in the world. On the other hand, one of the main carriers of accidents is the human factor, which is a very risky factor. The research has introduced six driver assistant systems to reduce driver workload and human error in order to reduce accidents, including navigation system, lane keeping system, forward collision avoidance, adaptive speed control, priority tracking system and emergency call system In order to carry out the above research, police and traffic police reports, forensic centers of the country, library resources, Internet searches, statistics and information about the number of casualties, injuries and damages caused by accidents in 2014 in Tehran province were collected and according to research and Various articles of cause of accidents and the share of each cause were determined in the total number of accidents. Then, with the introduction of each of the six driver assistant systems, the performance and role of each system were evaluated in the amount of crash reduction. In the next step, considering the cost of each of the six driver assistant systems and the benefits that each system makes in reducing accidents, using the economics of engineering, Benefits - Cost، were evaluated technically and economically. Then, according to the results of the economic evaluation obtained from the system that has the highest value for Benefits - Cost to the least amount was prioritized. In the end, the prioritized results were corrected with regard to the technical assessment and manufacturing technology and compared with the existing conditions and attitudes of car makers and legislators abroad and the accuracy of the prioritization was confirmed.

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.

A Multi-depot Heterogeneous Vehicle Routing Problem with Time Windows (MDHVRPTW) is a variant of the Vehicle Routing Problem (VRP)، in which the vehicles do not necessarily have the same capacity and they belong to different depots. Therefore، the MDHVRPTW involves designing a set of vehicle routes، each starting and ending at the depot، for a heterogeneous fleet of vehicles which services a set of customers with known demands. Most problems presented in this field are single-objective problems with the aim of minimizing the cost; however، the complexity of real problems usually doubts the use of single-objective problems. This paper considers not only the minimum travel cost، but also the distance travelled by the used vehicles and their loads. Since this problem is NP-hardness، the use of a meta-heuristic algorithm is obligatory. Therefore a meta-heuristic algorithm based on Multi-Objective Differential Evolution (MODE) is proposed. In addition، to show the efficiency of the proposed MODE، a number of test problems in small and large sizes are considered and then solved. The associated results are evaluated with the results obtained by the ε-constraint method and results showed that the gained function gap was less than 3. 5% in all the solved problems. Furthermore، to run the proposed MODE، a real-case study in an oil distribution company is carried out. Finally، the obtained results are reported and discussed.

Airports are considered as a main point for transporting passengers and goods. Meanwhile gates are the primary source, supplying service for passenger’s arrival and departure. The elaboration of airport operations and flight rates to major airports and their traffic caused gate assignment problem (GAP) has a particular importance. In this research, based on previous studies and different solution methods, tried to offer a model to assign the flight gates in the best possible way with considering existence constraints. The objectives of this study is based on declining passengers walking distance, reducing delays caused by gate occupation and decreasing each gate work time deviation from mean time of all gates to proper allotting flight activities. The proposed model can be used before initiating program or failure. The model ability was assessed, once in small size (comparing the results of Lingo with PSO algorithm in MATLAB), and once again in the real dimensions of data in Mehrabad Airport (comparing PSO algorithm in MATLAB response results with manually assigned), and it was investigated to present results from lower cost which represented improving goals in a reasonable time.

In recent years the complexity of projects, competitive business environment and organization resource constraints has resulted in attention to project management in achieving project objectives. Consequently employers looking to increase quality, reduce execution time and costs that are the main objective of the projects. It is therefore essential project managers with appropriate resource allocation and decision making process in order to meet the above objectives.Since most previous work has been done in terms of certainty and unrealistic conditions, there is a serious split in the decisions made by the project managers in the field of project management knowledge. Thus, one of the most prominent features of this study is to consider the conditions of uncertainty which is closer to the real world. In the present study, a fuzzy mathematical model is suggested for a network of activities which each includes several modes of execution in order to select the best mode among the possible modes and trade-off their criteria that will result to achieve the aforesaid purposes. To do this, a None-dominated Sorting Genetic Algorithm (NSGA-II) was designed to solve the suggested model. Then, a case study was conducted on the oil industry and the performance of the suggested model and the algorithm was examined. Furthermore, the level of attention paid by the project manager to the qualitative comments of the experts is shown using Fuzzy Alpha-cut Method. Testing the validity of such computational results indicates that when α=1, then the answers are oriented mostly toward the project contractual conditions which are the same conditions as expected by project manager. The more we are approaching from certainty conditions toward α=0 and indeed the uncertainty conditions, the worse conditions the answers resulted by solving the algorithm will have in terms of time, cost and quality. It is due to the fact that the project manager is taking the uncertainty conditions and risks into consideration by paying attention to the experts’ comments that will threat and worsen the general situation of the project.

According to the figures issued by UNCTAD, containerized trade is forecasted to grow by an average annual rate of 5.32 percent between the years 2003 and 2025. This paper studies yard crane scheduling problem between different blocks for a container terminal. The purpose is to minimize the total travel time of the cranes among the blocks and also, the total delayed workload in each block at different time periods. In other words, the forecasted workload within each planning period, additional crane capacity or insufficient crane capacity for each block, the time when cranes should be deployed and the routes of crane movements, all should be determined in a way that satisfies our objectives. Therefore, the problem is formulated as a mixed integer programming (MIP) model. The block pairs between which yard cranes will be transferred during the various periods, are determined by this model. Afterwards, the model is coded by LINGO which uses branch and bound algorithm for solving. The results determine the yard crane movement sequences among the blocks to achieve minimum total travel time for cranes and minimum total delayed workload in blocks at different periods. The biggest portion of the objective function value, for example, with 13 blocks, eight of which lack carne capacity is related to the total travel time of the cranes within the blocks. It is caused by the slow motion of the yard cranes and their large size. Also, total delayed workload calculated for four problems, decreases over the six time periods until it becomes zero. In other words, yard cranes are deployed in an optimal manner. Also, the results show the efficiency of the developed program.

The aim of this article is executive method determining for fuel consumption reducing in societies. We must determine urban vehicle choice dependant for effectiveness factors determining. After, with effectiveness factors optimized changing on vehicle choice, have been attempted the road user use public transportation. So, in the first step, we determine Rasht vehicle choice model and then with effective index using for travels, we determine increasing factors of public transportation using.