meisam jafari eskandari

In this research, a model for a stable closed loop supply chain with economic, social and environmental considerations, along with the risk arising from uncertainty in parameters, is presented. Random programming has been used for modeling this problem and also using the scale of value Exposure to conditional risk is measured by risk. The purpose of this model is to maximize network design benefits, reduce unemployment and increase job opportunities resulting from the construction of facilities and minimize the production of carbon produced through intranets, production centers, recycling, repair, re-production. Other goals include minimizing the risk posed by uncertainty in transportation costs and customer demand. In the end, in order to demonstrate the efficiency of the model, an example is solved with certainty and uncertainty with the risk measurement criterion, and the pareto optimal solutions are compared. The results of this study indicate that, with increasing risk, the profit from the supply chain network has decreased and should be costlier to face the risk.

AbstractToday, the use of game theory in supply chain management is very common. Game theory is used not only in pricing goods in the supply chain, but also in advertising discussions and examining the interaction of supply chain members. In this paper, the competition between two supply chains, each of which includes a manufacturer and a distributor, is considered. Demand for goods is a function of price and lead time. Competition under the game scenario is considered nonecooperation and with cooperation. Three types of scenarios have been developed under the game theory none cooperation (Nash-Stackelberg) and with different for analysis and decision-making related to price, lead time and shipping size. A unique equilibrium is shown in all games and detailed formulas are provided to calculate the optimal decisions of each chain. Numerical examples and analysis of the results of useful management insight to select the best strategies for chains in different market and operational environments.

In this paper, a bi-objective pharmaceutical supply chain network under uncertainty demand and transportation costs is modeled and developed. To control the uncertainty parameters, the robust optimization method is considering. The main objective of this paper determines the number and location of potential facilities such as drug manufacture centers and drug distribution centers by considering the minimizing the total costs and minimizing the maximum unsatisfied demand for distribution of drugs to demand zones. In this paper, production time, delivery, and drug perishable time is also considered in modeling. To solve the model, an example is designed and the multi-decision TH method is used. The results obtained the model shows this method is effective in finding the Pareto front at the right time. The results obtained the model shows this method is effective in finding the Pareto front at the right time.The results obtained the model shows this method is effective in finding the Pareto front at the right time.

In this research, a supply chain network has been designed to address social and corrupt situations. To evaluate the model, a small dimensional example was first designed and the model was solved with 3 decision methods (utility function, comprehensive criteria, and Goal programming). To compare the results of target functions and the effective responses obtained from the two-objective model, we compared the efficiency response indicators (averages of the target functions, the number of efficient responses, the most exponential index, the gap index, the distance index from the ideal point and the computational time). The decision method is a comprehensive criterion for acquiring average indices of the first objective function, the distance indicator from the ideal point, and the computational time more efficient than other methods. The ideal planning method has also proved to be effective in obtaining average indices of the second objective function, the number of effective responses, the most exponential index, and the efficiency gap index. Finally, the utility function method has also been more efficient in obtaining the problem solving index in less time. Finally, for comparing and choosing the most efficient solving method from solvency solving methods from topsis, the method of the comprehensive method is the most efficient method among existing methods.

E-Banking is a new type of banking service where banking services are provided using -environments. This kind of banking activity has spread throughout the world since 1991. Customers are migrating from traditional banking to modern banking with the increase of e-banking services and the provision of various tools for banking transactions. Accordingly, it is important to examine the progress of banks in these tools and the level of customer satisfaction. This study first analyzes the extent of customer satisfaction with these tools by examining customer transactions in Branch, ATM, Internet Bank and Mobile Bank Which has conducted this survey for a hundred thousand customers. After an initial analysis of the use of tools, Customers were clustered using k-Mean Method and Imperialist Competitive Algorithm with MATLAB software and grouped into seven clusters. Then the characteristics of each cluster are examined and strategies for each cluster are presented. In the final stage, we analyzed the strategies proposed by the Shapely value-based game theory approach and the most important strategies proposed.

Inventory management and production planning are two of necessary tasks for each manufacturing organization. In this paper, a production-inventory model with backlogging is expanded to identify the optimal value of inventory in multi-product organizations when demand is uncertain. The purpose of this problem is maximizing the total company’s profit considering inventory costs such as storage of raw materials and finished goods, backlogging contains both back order and lost sale, and purchasing, which is resulted in a nonlinear model. Also in this paper reworking of defected items and constraints such as finite production rate, warehouse space and capital are considered. The fuzzy set theorem is used to cope with uncertain inputs. To solve the proposed model a combined Honey Bee, Pareto and VIKOR method is applied. Results show that in the similar situations, applying this methodology leads to a maximum profit and minimum cost for manufacturing organizations.

Credit risk management, credit scoring and risk assessment of customers is an important issue in banking industry. Credit scoring is important because if the banks fail to earn resource allocation and create a balance between the processes of mobilizing and allocating resources, they are typically faced with many problems in the future. According to official figures released by the Central Bank of Iran in recent years, the rate of bad loans increased, since the systemic strict validation to evaluate and measure the credit risk of customers do not exist. In this paper, we try to predict customer’s recovery rate index with data mining techniques. Markedly, in recent years in the world new method for measuring customer risk rather than default probability measure has been considered, but due to low precision of forecasting models widely different approaches in research and modeling is investigated. The method used in this paper is support vector regression model whose parameters selection is optimized with genetic algorithm.

The Travelling Salesman Problem (often called TSP) is a classic algorithmic problem in the field of computer science. TSP is a special case of the travelling purchaser problem and the vehicle routing problem. It is focused on optimization. In this context better solution often means a solution that is cheaper. In this paper, TSP models to returned products to manufacturers seeking to minimize transport costs. Since the model is NP-Hard, to solve it we use bat algorithm.

Due to urbanization and wide use of technology in life, road traffic accidents annually cause 1.2 million deaths and injury of more than 50 million people over the world. Thus, one important traffic issues, is prediction of this road traffic accidents and presentation the solutions to reduce them.

Clustering is one of the most widely used models for the prediction and identification of patterns that is used in this research. For this purpose, road traffic accident data of Fars province accidents is considered. After pre-processing, data is clustered using self-organizing neural network algorithm. Then the accident are classified into three classes such as low, medium and high and accident patterns are recognized using decision tree model.

In this research the road traffic accidents in Fars province were clustered into 11 clusters. Then the accident were classified into three classes such as low, medium and high and accident patterns were recognized using decision tree model. Finally some suggestions are offered to reduce road accidents.

The main of this paper, is determining relationship between science and technology performance indicators used by private sector industry based on balanced scorecard. Evaluation of support by development organizations in the Ministry of Industries and Mines of IRAN in empowering the private sector in domains of science and technology includes four dimensions that in the first step, evaluation indices derived based on field studies and ultimately bilateral questionnaires are created. These indicators are grouped into some categories named strategic issues of that field and then between them are drawn conceptual relationship. Next, relationships between each of these issues, by means of statistics concept, are being assessed in the form of a multi-agent structure and ultimately, executive paths are being determined. This research will help managers to understand managing key indicators of success and can identify critical paths and the way to overcome critical situations.