فصلنامه پژوهش های مدیریت در ایران
سال هفدهم شماره 4 (پیاپی 82، زمستان 1392)

Credit institutions to provide variety of facilities to their customers, need to comprehensive studies by qualitative and quantitative aspects of their applicants. By this way, accomplish a complete evaluation of repay ability measure and calculate the refund facilities probability and finance services by them, these reviews generally validation name. The purpose of this study was ranking customer groups and specifies the best part of them until brokerage firm do its credit allocation process mechanically. Here, after the preprocessing of the data, they are processes in the RFM model. Then SOM neural network as one of the clustering algorithms will change customers to 10 cluster. Using the proposed model, the clusters will rank. The top clusters, identification and facilities grant operations to the members of these clusters will do. Finally, three clusters 5, 1 and 7 defines as top clusters that they are the target customers. Coefficient facilities granted to the top three clusters respectively are 0.271, 0.173 and 0.556.

In traditional covering problem, covering levelof receiving the services is independent ofdistances between nodes and facilities. However in gradual covering location problem (GCLP) the covering objective depends on the distance of customers from the service centers. Hence increasing incustomer-facility distances will results in decreasing the covering level. In most of covering problems, researchers only consider the distance factor. However; in the real word there are some other important elements such as population, availability, distance and etc. which affect the location of service center. Increasing the number of demand nodes and criteria’s will result in increasing the nodes evaluating computational time and inconsistency rate. This paper proposes a combined Simulated annealing (SA) and Artificial Neural Network (ANN) approaches to solve the multi criteria GCLP.In presence of few nodes; score of nodes are calculated usingAnalytic Hierarchy Process (AHP) method. Moreover by increasingthe number of nodes the score of every node will be calculated using ANNinstead of AHP. Statistical test of signed rank test shows that there is not a significant difference between the result of ANN and AHP methods. The comparison results between the exact solution method and the proposed algorithm confirms the efficiency of the proposed solution approach.

Nowadays, reverse logistics is one of the major activities of supply chain management that are included all of physical activities related to returned products (e.g., collection, recovery and recycle). So that, designing and implementing of appropriate reverse logistics, not only lead to increase customer loyalty, but also it will be reduce the inventory and transportation costs. This paper presents a mixed-integer linear programming (MILP) model for the network design of forward and reverse logistics, which minimizes the costs consisting of the fixed cost, material cost and construction of the potential center cost. To validate this model, a number of test problems are generated, and then its parameters are determined. The presented model is solved by the Lingo software and the associated computational results along with the sensitivity analysis are illustrated. Additionally, the returned products with different demands for one and two products are illustrated. Finally, the remarkable conclusion is presented.

The necessity of restructuring university budgeting from program-based to performance-based budgeting (PBB) leads many studies to be conducted. Reviewing the literature, we observed that there is no mathematical model including Dual PBB Structure in University.The purpose of this paper is to construct a PBB model for university so that the budget is allocated not only to plans based on their priorities, but also to faculties based on approved student per Capita by the Ministry of Sciences, Researches and Technology.Considering the various criteria in the university system and because of fuzzy and stochastic uncertainties in the problem parameters, two scenarios were analyzed.1) Crisp Lower Bound Robust Fuzzy PBB (CLB-RFPPB) model2) Fuzzy Lower Bound Robust Fuzzy PBB (FLB-RFPPB) model.A significant point in Both of them, is to use performance Indicators--calculated based on data envelopment analysis approach (DEA) regarding a basic model of input-oriented CCR— determined the importance coefficient for each educational groups in order to allocateing budget. In addition, the weights of Goals and the priority of each plan were determined using some paired comparison questionnaires completed by experts. The PBBGP model has 5 goals, 1142 constraints and 994 decision variables.The results of models solution- that are presented on two macro and Operational Levels- and simulation of Crisp and two RFPBB models, Demonstrate high level capabilities of RFPBB models in response to the uncertainty in the parameters of problem and also managing the level of decision risk

After the industrial economy span that caused many changes in the human life and the human systems، now we are facing a new era in the electronic revolution. One of the symbols of this electronic revolution is Mobile Commerce. Nowadays، this new style of trading has been used in many parts of the globe especially، in the developing countries. Now the fundamental question is how to design and explain the business development model. To solve this issue، the present exploratory study has been conducted، with the scoring and weighting the extracted factors and making items for them، by the Delphi panel of experts (during four rounds)، the researcher found three dimensions (technological، social and personality) and twelve new variables. These variables formed the conceptual model of Mobile Commerce. The conceptual model has been examined in the Iranian Virtual Society with designed questionnaire and using AMOS software. The collected data were analyzed using Structural Equation Modeling. For this purpose، initially the structural relationships have been tested by using Confirmatory Factor Analysis، then with a Path Analysis approach the hypotheses have been examined. At the end this research، with the future studies approach and adding the time dimension to the three dimensions، makes the model more dynamic.

Determining how to assess learners and contents of e-learning are essential activities in its processes. These activities are conducted by a professor or teaching assistant and they determine student assessment methods، such as holding an online test or periodic homework assignment. if the organizers can be aware of the effectiveness of each activity in the quality of learning،then besides saving considerable time and resources to stakeholders courses، which transfer the content useful and realistic assessment of students and will ultimately improve e-learning. In this paper، first we use unsupervised techniques of data mining for clustering and describe the present status of learners، and extract hidden rules in e-learning data using rule mining and will discover the effective contents in desired results. Next، using supervised methods we predict results of courses. Using real data of an electronic course provided and with designing four different methods for data sampling and training system، predictions were performed and the methods were validated with an accuracy rate of 92. 86%. We have shown that the methods of this study can help teachers for a better understanding of learners and impact of such training activities required، such as describe characteristics of learners based on the discovery of hidden patterns in the scores of their acquired and determine the most effective learning activities and decide about real measures of learners.

Determining how to assess learners and contents of e-learning are essential activities in its processes. These activities are conducted by a professor or teaching assistant and they determine student assessment methods، such as holding an online test or periodic homework assignment. if the organizers can be aware of the effectiveness of each activity in the quality of learning،then besides saving considerable time and resources to stakeholders courses، which transfer the content useful and realistic assessment of students and will ultimately improve e-learning. In this paper، first we use unsupervised techniques of data mining for clustering and describe the present status of learners، and extract hidden rules in e-learning data using rule mining and will discover the effective contents in desired results. Next، using supervised methods we predict results of courses. Using real data of an electronic course provided and with designing four different methods for data sampling and training system، predictions were performed and the methods were validated with an accuracy rate of 92. 86%. We have shown that the methods of this study can help teachers for a better understanding of learners and impact of such training activities required، such as describe characteristics of learners based on the discovery of hidden patterns in the scores of their acquired and determine the most effective learning activities and decide about real measures of learners.

High death rate as a result of the Road Traffic Collision (TRC) in Iran، and the concomitant financial and economic damages، is alarming and requires an immediate attention and proper solution. Taking the culture of safety into consideration، this research aims to offer a model for enhancing road safety. In order to achieve this goal، I have done a comparative study of successful methods adopted by several countries، in dealing with this problem. Based on these findings، I have developed a model and designed Delphi Panel In three Periods Dimensions، components، and the required indicators were found and their importance in Hierarchical analysis was determined. The panel members are composed of specialists and authorities in the area of road safety. Findings of this research show that adoption of any and each of the above mentioned components would enhance the road safety culture. Adopting a flexible approach in each circumstance constitutes the most important aspect of my model. This is because، under each particular circumstance، by enhancing certain factors، we will be able to achieve our goal of enhancing the culture of safety

Mobile telecommunications tremendous growth in the 21st century made ITU define wireless communications as the driver of ICT in the world. Advent of this technology to Iran، like every other new technology، leads to superior changes in economic، socio-cultural، political، defensive and security aspects. None of the research done on national security has directly focused on mobile telecommunications industry. The aim of this paper is to propose a structural model for the enhancement of mobile telecommunications with regards to national security interests. To fulfill this aim، we first define the research question and problem solving method using systematic approach. The research guidelines are then studied one after another. After identifying influential elements and their interrelations، the development model، which is a directed graph، would be introduced using Interpretive Structural Modeling (ISM) method. Key elements of the model are: National Security، National Interests، Foresight، Understanding the Environment، Problem solving design approach، Breakthrough Thinking، Selected theory-driven development، Communications Regulatory Agency، Strategic Intentions and Upstream Documents and Macro trends. The final model presents how Strategic Intentions turn into action plans under the influence of Global Macro trends. Proposing segmentation based on different layers of the model for better implementation and the long-term effects of national decisions and accomplishments on global drivers constitute other achievements of the modeling.

Football is an industry that Branding and its foundation, brand identity, it has extremely important in. so that researchers have proposed specific brand models in countries with football. In this research, using descriptive analytic methods and with the purpose of application, data was collected in two qualitative and quantitative phases. The statistical population consists of 10 professionals and experts in related to research and Spectators of six teams in the Premier League that would be present in the stadium. Sample size was 300 cases in Phase I and 720 in Phase II. which using simple random sampling method and equally, the fans selected. Accordingly, researchers found need of new theoretical insights based on brand identity(Koo(2009), Schilhaneck(2008), Zucchermaglio(2011), Silveira(2011)&Richelieu(2010), and considering the brand identity models, in few countries with football brand (Germany, Spain, France and S.Korea), and with the use of experts and fans perspectives, the brand identity model of teams in the league in season 92/91 offer and Finally using CFA and SEM model provided a new perspective on the dimensions of the country football brand identity, that are on the basis of factor loading as following: Success(./97), Delivery(/.92), Native Region(/.89), Star Player(/.88), Logo, Fans, History, Traditional Rival(./87), Tradition(./85), Team performance (./82), Stadium(./72) and Non -Player personnel (./51).