فهرست مطالب محمد باقر فخرزاد

Nowadays, the issue of supply chain network design has garnered significant attention in supply chain management. Researchers have focused on the competitiveness of markets and the pursuit of greater benefits. Therefore, this article presents a multi-objective mathematical model for the    design of a dynamic and integrated supply chain network, specifically addressing a competitive and sustainable closed-loop supply chain for a perishable commodity. The proposed model        incorporates competition between two supply chains, considering environmental, social, and resilience factors. To solve this competitive model, a two-step approach is introduced. In this two-stage approach, the first stage involves calculating equilibrium values for competitive decisions using game theory. Then, in the second stage, an innovative multi-objective algorithm based on Pareto optimization is employed to solve the network design problem, taking into account the results from the competitive stage and addressing the complexities of the proposed model. Finally, the efficiency of the proposed model and solution approach is evaluated using a numerical example analyzed graphically and statistically. The results and graphs demonstrate that the proposed model performs well and successfully achieves the intended objectives.

In a green supply chain, preventing the disposal of used products and reusing them will help to protect the environment. The most important step in the closed-loop supply chain is the disassembly and recovery of used products and their reuse in producing new products. In this paper, we propose a new mixed-integer programming model to consider a multi-period multi-product disassembly scheduling problem based on parts commonality and partial disassembly. Considering partial disassembly is the main contribution of this paper. When the disassembly is partial, the parts with no demand will not disassemble; so, a lot of time and cost will be saved. The proposed model is solved using CPLEX Solver, and sensitivity analysis is deployed to validate the model and determine the proposed model's practical results. Numerical experiments shows that the CPLEX solver has a good efficiency for solving the developed model in small and medium dimensions.

The economic conditions prevailing in the country, sanctions, rapid changes and transformations of the markets, and the complexity and disorder of the market environment, have made it necessary to pay attention to the anti-fragility approach in the industries. One of the most interesting industries is the textile industry. Hence, due to the strategic nature and having a large domestic consumption market, the textile industry must be anti-fragile in order to quickly adapt to new conditions and maintain and improve its competitive value in a safe and stable condition. This research was conducted with a review of the literature in order to achieve the anti-fragility of textile industries throughout the supply chain. We identified the anti-fragility components and weighed them using the best-worst method. Then, we reviewed the literature on the textile industry and interview the experts in this industry, to identify and evaluate the fragility factors. By using the CoCoSo method the most important factors that disrupt anti-fragility were ranked. Finally, the results of the research showed that the components: unexpectedness, Adequate supervisors, and Attraction are the most important components. Also, the results obtained from the CoCoSo method showed that the of ''Lack of investment in the (clothing) industry", "Saturation of the market with counterfeit brands due to the structural weakness of the distribution and smuggling network", and "Undeveloped distribution network" are the most important in the supply chain of the textile industry.

The rapid changes and transformations of the markets and the complexity and disorder of the market environment and the conditions of the economic situation in the country due to the sanctions have made it necessary to pay attention to anti- fragility approach in the industries. The textile industry is one of the strategic industries that should be considered and rewarded due to the large domestic consumption market. Various textile industries can quickly adapt to new conditions and maintain and even improve their competitive value in a safe and stable condition. The current research was conducted with a review of the literature in order to achieve anti-fragility of textile industries throughout the supply chain. In this way, the anti- fragility components were first identified and weighed using the updated best-worst model technique under conditions of uncertainty. Then, by reviewing the literature of the textile industry and interviewing the experts of this industry, stressful factors were identified and evaluated using the TOPSIS method in order to identify and rank the most important factors that disrupt anti-fragility. Finally, the results of the research showed that the components: unexpectedness, sufficient supervisors and redundancy are the most important components. Also, the results obtained from the TOPSIS method showed that the lack of access to foreign capital and the main export markets, the wear and tear of the machinery of the spinning chain industry, and the import of clothing from low-cost producing countries are the most important in the supply chain of the textile industry.

The main purpose is to analyze social entrepreneurship by presenting a multi-objective model of a multi-level supply chain of blood products. The present research is applied in terms of purpose, descriptive in nature, and quantitative in terms of gathering information. The studied community of new local bases, mobile bases as well as transportation equipment for the effective and timely transfer of blood products to the candidate provincial centers of the country in the time domain of 1400 is collected. In order to minimize the costs of the entire supply chain network and maximize the attractiveness of the transmission centers, the proposed mixed integer two-objective mathematical model was designed and carried out by epsilon constraint method with coding in GAMS24.1.2 software. The results showed that increasing the number of blood donation sites, facility capacity and attractiveness leads to improving the attractiveness and performance of the supply chain of blood products.

Cellular production is one of the important applications of group technology in production. With the development of modern industrial technology, many manufacturers use it as a solution to implement complex and realistic scenarios that increase the productivity and flexibility of a production system. Cellular production includes cell formation, cellular and intracellular arrangement, operation scheduling, and resource allocation. The process of formation and grouping of machines to produce families of parts to minimize the cost of moving materials among cells is called cell formation. In other words, cell formation in cell production systems and assignment of machine groups and family of parts to these cells is done to minimize the total cost and increase flexibility and productivity in production. The layout design is also related to the position of the cells relative to each other and the position of the machines in each cell relative to each other. In some production units, the placement of cells in relation to each other and even the placement of devices in each cell is not done properly, which increases the movement of materials, semi-finished parts, and consequently, production costs. On the other hand, with changes in customer needs and demand and competitive market conditions, the combination of existing cells and their arrangement in one period may not be appropriate for another period, and it is necessary to make changes to reply to customer needs and remain competitive. The possibility of making changes in cells combination, placement inside and between cells is called dynamic cell formation. In other words, dynamic cell formation involves changing the position of the cells relative to each other and the proper placement of the machines in one cell so that it is possible to move the machines to a new position or another cell and increase or decrease them.

Operation scheduling and assigning human resources incurring a notable proportion of expenses in the cell formation. These issues seem more important when financial resources are limited. In this research, dynamic cell formation problems based on scheduling, allocation of workers, and constraints of financial resources on machines and workers are simultaneously investigated, Accordingly, the present study seeks to minimize the total costs, including the costs of machines, workers, and transportation of parts. At first, a mathematical model was presented. The model was then linearized and validated. After that, a genetic algorithm was proposed to solve the problem where the parameters were adjusted and selected by using the Taguchi method. Sensitivity analysis was also performed based on the related parameters in constraints of financial resources of machines and workers.

The results showed the accuracy of the model and its validation. It was also shown that the proposed algorithm is highly efficient and can be used for medium and large-sized problems where it is not impossible to find the optimal solution.

Sensitivity analysis showed that the constraints of financial resources for purchasing machines have a greater impact on the objective function than workers' financial constraints, which is of high importance.

In this paper, data envelopment analysis (DEA) was discussed for non-homogenous mixed network structure where the outputs of each stage can enter the next stage in the same or another layer as inputs or come out from the system as final products. In this model, in addition to intermediate inputs, each stage can also have independent inputs. For this purpose, a mathematical model has been developed in which the independent inputs and final outputs for the components of the decision making units (DMUs) are examined. To show the efficiency of the model, real data for 20 DMUs were used and the results were compared with those of traditional models. The results obtained from the proposed method eliminate the shortcomings of traditional methods for proper detection of units that are on the efficient border (with an efficiency of one). The developed method can provide the managers and decision makers with a more accurate understanding of the performance of components of DMUs.

Vendor management inventory, as one of the inventory management methods, reduces the cost of inventory in the chain, quick response to customers, increased service level, customer satisfaction, and improves collaboration between the members of supply chain. In this study, a single-product bi-level supply chain model with one manufacturer and several retailers under a vendor management inventory system to select retailers under competitive conditions is investigated. In this study, the retailer selection problem formulated as a Stackelberg game model with consideration of manufacturing as a leader and retailers as followers to help the manufacturer and optimally select his retailers to form a VMI system. The manufacturer delivers the products to the selected retailer at the same time. Also, demand for the product in the retailers market is the decreasing function of price. Due to the prevailing policy and the agreement between the manufacturer and the retailer, the manufacturer is also responsible for managing the retailer's inventory. Therefore, the manufacturer will bear all the costs of maintaining the inventory, whether with himself or the retailer. In return, to accept the responsibility of inventory management by the manufacturer, the retailer pays the manufacturer a fee per unit as inventory costs. The objective of the model is to maximize manufacturing profits at the first level and maximize profits of any retailer at the second level. Model decisions are finding the optimal quantities of wholesale price decisions, product's replenishment cycle time, backorder, retailer selection, and determining whether or not to set up a production line for the manufacturer and retail price quantities for the retailer. In order to validate the proposed model, sensitivity analysis was performed on some parameters. As demonstrated by our numerical studies, the optimal retailer selection can increase the manufacturer's profit by 91% and the selected retailer's profits significantly compared to the non-selection strategy.

The Aircraft Maintenance, Repair, and Overhaul (MRO) has always played a key role in the aviation industry which is responsible for restoring aircraft to flight conditions based on a set of technical instructions and implementation of repair processes. According to researches, the most important reasons why airlines choose MRO centers to provide services are quality, turnaround time (TAT), and cost, respectively. In this research paper, the application of lean, its principles and techniques, and in particular the study of its compatibility and effectiveness in order to reduce TAT in the aircraft repair and overhaul industry has been considered. Accordingly, at the beginning and after the comprehensive review and analysis of the literature, the subject of a comprehensive list of principles of lean production has been studied and the indicators have been considered according to the characteristics of the aircraft repair and overhaul industry. Then, based on the obtained indicators, a questionnaire was prepared, and based on it, more than 25 hours of interviews with experts, including deputies, managers, and experts of the aircraft repair and overhaul industry in the case study were conducted. Based on the Yin analysis method (2003), the data of the questionnaire were analyzed and evaluated in order to identify the influential factors of service time during the aircraft maintenance check. Based on the research findings, the existence of defects in some areas, such as the lack of inventory management and alternative planning for serviceable components have created significant effects in increasing the service time of aircraft.

This paper aims to develop a non-homogeneous NDEA model for measuring the efficiency of a mixed network with an open structure at each stage, interlayer connections, and uncertainties in inputs, outputs, and intermediate processes. 

To model the problem, a new model has been proposed first to determine the efficiency of each layer, based on the average weight of the layers. Then, the model has been developed to calculate the total efficiency. The Bertismas and Sim approach has been used to consider the uncertainty of inputs, outputs, and intermediate processes. Pistachio orchards in Yazd province have been selected as a case study, and relevant data of 10 cities in the Yazd province has been collected from the Agricultural Jihad Organization of Yazd province. 

The results indicated that with increasing the deviation in uncertain network data from 0.01 to 0.1, the average total efficiency decreased from 0.933 to 0.915. Such a reduction was also observed in the efficiency score of each of the DMUs. Reducing the average levels of efficiency and tightening the constraints on the proposed models reduced the number of units on the Performance boundary and eliminated the need to use super-efficient models to re-evaluate the efficiency. In other words, the computation of the total efficiency of decision-making units and sub-layers was enhanced. Research limitations/implications : The proposed approach was developed based on the model of Charans et al. (1978). As a limitation, the weighted sum of the outputs was less than or equal to the weighted sum of the inputs. The proposed model can be developed further to include other factors, such as undesirable outputs.

Findings can provide a more accurate understanding of the performance of the components of decision-making units to managers and decision-makers. This study highlights the usefulness of the proposed models as a decision tool in agricultural units. 

The developed models can be used in various social contexts. According to the case study, it is overemphasized that water resources management is significantly important for Yazd as a province located in an arid and desert region. Therefore, increasing the efficiency of orchards by identifying and improving inefficient processes, can lead to helpful-agricultural consequences in this province. 

While many studies have been conducted in the field of NDEA, this is the first study in the field of open non-homogeneous mixed networks considering uncertainty in all data, simultaneously.

Although productivity is at the heart of the success of any supply chain, a broader approach that encompasses economic, environmental and social aspects is a better way to achieve sustainable success. The increasing importance of measuring sustainable supply chain performance is due to competition between independent organizations whose supply chains are in competition. To this end, the present study seeks to investigate the role of supply chain management strategy in sustainable supply chain performance. Due to the increasing trend of using structural equation modeling and its power in testing hypotheses, the designed model was tested using partial least squares approach. The designed model constructs include four dimensions: "Supply Chain Management Strategy", "Corporate Social Performance", "Corporate Economic Performance" and "Corporate Environmental Performance" and the results show that "Supply Chain Management Strategy" on "Performance Dimensions" Corporate Social Performance, Corporate Economic Performance, and Corporate Environmental Performance have a positive and significant effect, and the dimensions of "Corporate Social Performance" and "Corporate Environmental Performance" have a positive and significant effect on "Corporate Economic Performance".

Due to the increasing requirement in the manufacturing industry and the rapid development of technology, the traditional supply chains face many challenges such as changes in demand and transportation problems so that they need the flexibility in capacity, lead time, and distribution channels. In this research, in order to overcome the problems of the traditional supply chain, a supply chain based on ICPT in the MTS production environment called smart supply chain is considered where including four levels of manufacturer, warehouse, distributor, and customer that the goal is to maximize profits and minimize lead time. At first, a Mixed Integer Non-Linear Programming was proposed for the problem, then a sample problem was solved by the augmented ε-constraint technique in GAMS and the results were analyzed. Two scenarios of increment of demand and adding a new product were examined which all of them showed the accuracy of the model and the efficiency of the proposed method.

Hospital waste collection is one of the most important issues in urban service management. In this research, a mathematical model is developed to partition demographic areas in the hospital services system. In this model, regarding the needs of some service providers, the hierarchical structure of the partitions is considered. For this purpose, in a unified decision-making process, populations are divided into main partitions, and then, each one is divided into a number of sub-partitions. The purpose of this type of segmentation is to provide an ordered structure to control the service flow from the operational level to managerial level. Since partitioning is an NP-hard problem, it is necessary to use meta-heuristic algorithms to solve numerical examples in the real world. Here, genetic and gray wolf algorithms have been developed to solve large-scale problems. Despite the high efficiency of both algorithms, the computational results showed that the gray wolf algorithm is more capable in solving large-scale problems. The results of this study can be used as a management tool in solving types of population-based partitioning problems, including the problems of health systems.

One of the important issues of religions is the issue of sectarianism. On the one hand, the fields of the emergence and expansion of sects are multidimensional and complex, and on the other hand, there are numerous systems involved in problem solving. The Studies in the field have shown that most studies have deviated to some cases of the diverted sects the and left the common teachings of these sects. Therefore, in this study, we first describe the common causes of the emergence of sects and, then, use dynamic system and gather the causes of their extention in a diagram and describe the effect of each cause. Accordingly, the causes of the growth of the sects are simulated and the results of each of the ten-year time horizons are investigated by creating three scenarios. The original amount of sectarianists is estimated at 300,000 people. The highest growth rate is when coercive dealing is on the agenda and the best situation is when authorities focus on vacuuming.

In this paper, a multi-period multi-level multi-products green closed-loop supply chain model under uncertainty is developed, which objectives are to minimize total costs, minimize emissions from vehicle displacement between levels, and maximize the reliability of delivery for suppliers. This network is including supplier centers, production/resuscitation centers, distribution/collection centers, customer centers, and disposal centers. A new Linear Integer programming model is formulated. Besides, fuzzy parameters and multi-objective function are used to approach the real world. In this regard, a deterministic two-step approach is used to consider the uncertainty in the proposed model. Finally, the performance and efficiency of the proposed model and solution methods are simulated in the numerical example and examined and suggestions are presented for using this model in the real world.

In recent years, the location of mobile facilities has been highly regarded in design of health system. One of the important areas in this field is design of blood collection and distribution systems. Since blood is as perishable and vital goods and donation of blood is a voluntary work, supply blood and blood products is one of the most challenging issues in the supply chain in emergency and non-emergency situation. In this paper, we propose a four-echelon two-stage stochastic model to supply whole blood and its products in disaster. The hospitals, regional blood centers, local blood centers and bloodmobile facilities are considered as main elements of blood supply chain. It is assumed that the blood collection from the donors just done in bloodmobile facilities and local blood centers. Also, processing operation of blood products from collected whole blood are done only in the regional blood centers. In the designed supply network in this paper, every hospital could provide needed blood products from other near hospitals in the emergency situation as well as other fixed and mobile blood centers. One of the most important issues in the blood supply in disasters is delivery time of blood, so here; we consider an upper bound to blood delivery time to the affected areas and the objective of our presented model is blood supply in the standard time so that the total supply cost be minimized. In the other hand, the time solution is very important to obtain an acceptable solution in a reasonable time. We present a heuristic approach based on genetic algorithm and exact method to solve the proposed MIP model. The locations of fixed and mobile facility are computed through genetic algorithm then other variables are calculated by solve model with CPLEX. In order to validate the proposed approach, we generate 8 examples with different sizes and numerical results are presented. Also the results of comparison our approach with exact and metaheurisitc method are presente.

In this paper, we investigate a new optimization for territory design in the distribution system and allocation of the customers to supply centers which are considered as territory centers using MIP model. The objective is to balance the work load through minimizing the maximum differences the minimum customers allocated to the various centers. The study constraints guarantee continuity of the territories and the lack of gaps in the territories. Also, other constraints include allocation of a center to each territory and exclusive allocation of each customer to only one territory. Since, territory design and positioning are among NP-hard issues, in order to solve real-world case and big problems we have to propose meta-heuristic algorithms. For this purpose, in this paper, a grey wolf optimizer and a salp optimizer algorithm are proposed. Based on the literature review, it is very difficult to use encoding-decoding solution  without any modifier algorithm. Therefore, we design a novel solution scheme based on a minimum spanning tree in order to obviate the complexities, guarantee the continuity of the territory structures and the lack of gaps, and generate feasible solutions. Computational results on random instances showed that the proposed algorithms can effectively help to generate reasonable responses. The model proposed here could be a useful tool to aid the decision-making in distribution management, as well as for the better organization of any distribution.

In this paper, a new model for managing multi-product multi-level supply chain using predictive control of Scenario-based model is presented which it can handle supply chain with Stochastic programming of the uncertainty, which is caused, by demand and random lead time. In addition, it guarantees a certain level of customer service in the form of horizon with 95% probability. Probabilistic waiting time may lead to lash effect throughout the entire chain And causes shortages at different levels therefore cost of facing shortages is considered in the model. After modeling problem is solved in deterministic and Probabilistic state and to solve larger problems Imperialist competitive algorithm is used. The results indicate that SCMPC is computationally very efficient and offers significant advantage over the robust and stochastic optimization.

In the articles that have been investigated by the vendor managed inventory less attention to environmental pollution or emission of green logistics is considered. In this paper, the issue of green backorder vendor managed inventory in two-echelon supply has been considered. But innovation in green conditions of this article or reducing the amount of pollution is as a function intended purpose. In view of customer facing staff shortages in the supply chain, two-echelon warehouse and limits the number of orders is considered. The first objective is to maximize profit of vendor, which includes sales minus production and distribution costs and facing to shortage cost. The second objective function is minimizing the amount of pollution caused by the movement of goods. According to the model as a model of bi-objective, non-convex and non-linear algorithms is solved by GAMS and Epsilon-Constraint on a small scale can be used to obtain the Pareto front and compare with traditional inventory management in shortage state and for large scale non-dominated sorting genetic algorithm (NSGA-II) was used to solve the model. To adjust the algorithm parameters NSGA-II Taguchi method has been exploited in this article. The results of this research is that allowed the shortage situation, the lack of cost reduction and environmental pollution reduction, the greater the amount of shipping and orders as well as the lack of a less optimal to reduce the cost of inventory shortages.

This study aimed at predicting employee's productivity according to executive intelligence and organizational intelligence in Islamic Azad University Isfahan branch. Descriptive statistical correlation was conducted in this research. The study population included a total of 515 of all managers and staff of Islamic Azad University Isfahan branch. 220 people were chosen as the sample population using convenience sampling and Cochren formula. Data were collected with Productivity StandardQuestionnaire (Hersey & Goldsmith 1980), Executive Intelligence Questionnaire (researcher-made) and Organizational Intelligence Questionnaire (Albercht 2003). To analyze the data, simple regression analysis were used. The results of regression analysis showed that organizational intelligence (β= 0.426) and executive intelligence (β= 0.440) can predict employees’ productivity at a significant level (p

In this paper, the problem of job scheduling and preventive maintenance activities is investigated simultaneously in an unrelated parallel machine’s environment with machine deterioration. Due to the existence of deterioration phenomenon and its effect on the job scheduling, the aim of this paper is to find the ideal time, the optimum number of maintenance activities and the sequence of the jobs, for minimizing the sum of job's completion times. Preventive maintenance activities may be needed because of tool wears or machine deterioration in a job scheduling horizon. Here, two different deterioration time function and tool wear are studied, in which the machines are reverted to the original condition after any preventive maintenance activity. The duration of each activity is considered as a function of its starting time and its position on the sequence of job scheduling. A mathematical model is proposed and due to its complexity, a simulated annealing Mehta-heuristic algorithm is used for solving the model. Finally, numerical examples are given and sensitivity analysis with various parameters is applied to evaluate the performance of the proposed method.

This study aims to rank the indicators and dimensions of the organizational justice. It is a cross-sectional study with a survey approach. The population of the research consists of the entire staff of the Imam Khomeini Relief Committee (RA)in Khorasan. A sample of 280 participants who are selected by cluster random sampling method, were instructed to use the Cochran formula. The Standard questionnaire of justice, "NyhvfandMoorman" has been taken as the main tool for gathering needed information which has reported acceptable reliability and validity coefficients. The results of the data analysis were determined based on the TOPSIS technique. It is found that the ratio of the caring and respect of the heads toward the clerks is on the top with an outstanding difference with the others. Rating of the staff’s hours of working with a ration of 0/2633 has been taken as the topest. Rating of the organizational justice in the study includes communication justice, procedural justice and distributive justice. Another finding of the study was that there is no meaningful difference between the components of the age, work experience and education level of the staff and the perception of being fair in their organization

Learning logic of exceptions is a substantial challenge in data mining and knowledge discovery. Exceptional phenomena detection takes place among huge records in a database which contains a large number of normal records and a few of exceptional ones. This is important to promote the confidence to a limited number of exceptional records for effective learning. In this study, a new approach based on the abnormality theory, information and information granulation theories are presented to detect exceptions and recognize their behavioral patterns. The efficiency of the proposed method was determined by using it to detect exceptional stocks from Iran stock market in a 30-month- period and learn their exceptional behavior. The proposed Enhanced-RISE algorithm (E-RISE) as a bottom-up learning approach was implemented to extract the knowledge of normal and exceptional behavior. The extracted knowledge was utilized to design an expert system based on the proposed abnormality theory to predict new exceptions from 6022 stocks. The superior findings show the results of this proposed approach in exceptional phenomena detection, is in accordance with expert's opinions.

In this paper, minimizing the number of tardy jobs in two-machine flowshop scheduling with deteriorating jobs and release times is discussed. In most of basic scheduling problems, the processing time of the jobs is assumed to be constant. This assumption is true in some cases, but because machines and tools depreciate and their efficiency reduces during time, this assumption cannot be true in all cases. In addition, in some industries like steel industry, job's delay for process results in longer processing time. These kinds of jobs are introduced as deteriorating jobs, so a job is deteriorating whenever its processing time is not constant and is dependent on scheduled jobs. In this thesis, scheduling problems with deteriorating jobs are studied. A general classification of these problems is presented and literature review is studied. Then, flowshop scheduling with deteriorating jobs is discussed, and minimizing the number of tardy jobs is assumed as objective function. It is proven that the complexity of the problem is NP-hard. Therefore, a heuristic algorithm is proposed to achieve near optimum solution in a short time. Besides, an exact branch and bound algorithm, along with utilizing heuristic algorithm as upper bound, was proposed to achieve an optimal solution. Computational results demonstrate that branch and bound method solves problems with 24 jobs in the set High and 22 jobs in the set Low in a reasonable time. Results show that a high percentage of nodes are fathomed by lower bounds and dominance rules that shows the capability of the branch and bound algorithm. Also, it is shown that the average ratio of optimal solution to the heuristic one is at most 1.15 which is smaller in contrast with other studies in the related field in the literature. Finally, according to the efficiency of the presented approach, sample problems with large dimensions are generated and solved and their results are displayed.

Learning logic of exceptions is a considerable challenge in data mining and knowledge discovery. Exceptions are the rare phenomenon with positive unusual behavior in a database. Creating an efficient framework to increase the reliability in the detection of exceptions in the knowledge and learning is quite important. This paper presents a novel framework to promote the confidence to a limited number of records (exceptions) for effective learning of exceptions. In this study, a new approach based on the abnormality theory and computing theory is presented to detect exceptional phenomena and learn their behavior. First, Renyi entropy function is implemented to detect exceptional data which is differentiated data according to their hidden knowledge. Then, the novel E-RISE algorithm which follows bottom-up learning strategy is introduced to learn exceptional data behavior. Efficiency of the proposed model is determined by applying it to the Iran stock market data. Mining the number of 1334 stocks data points, 2.6% of them had exceptional behavior. The extracted rules represent the exceptional stocks attitudes. After that, an expert system is designed to use the extracted knowledge for recognizing new exceptional stocks. Faced with new stock, this expert system can recognize exceptions by comparing its characteristics with normal and exceptional behavior. Exceptions behave in compliance with exceptional rules or in contradiction with any normal pattern. This acquisition knowledge is the basis of exceptional portfolio selection which aims to make exceptional wealth for investors. Findings of the proposed method are compared with the outcomes of applying traditional methods as decision tree and support vector machine which is considerable. The results show the capability of the proposed method in exceptional data detection and learning their behaviors.