Azam Keshavarz Hadadha; Zahra Jalili Bal; Siamak Haji Yakhchali
Abstract
Project Portfolio Selection is one of the strategic decisions on the level of management in project-oriented organizations; which is one of the most important and effective stages in project portfolio management. In other words, after identification and evaluation of different projects, optimal combination ...
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Project Portfolio Selection is one of the strategic decisions on the level of management in project-oriented organizations; which is one of the most important and effective stages in project portfolio management. In other words, after identification and evaluation of different projects, optimal combination of projects must be selected based on different criteria. Since commonly resources of organizations are less than required resources on projects that are ahead of the organization; so project selection among different project portfolio and decision making about these issues are inevitable in organizations. So durability of organizations depends closely on the way of modeling and used approaches for project selection problem. In previous researches related to project selection problem, generally the problem of project clustering have not deeply been considered while project clustering may allow that projects to reach the highest return. In this article, we will propose a model for clustering, evaluating and selecting projects. At first projects will be clustered in various portfolios by K-MEANS algorithm, then projects of each portfolio will be evaluated and prioritized with analytical network process (ANP). Finally, projects from each portfolio will be selected based on knapsack problem
Ali Bonyadi Naeini; Saeed Yousef; Mohammad Ali Faezirad
Abstract
Today evaluation of customers to classify the quality of providing services is one of the main challenges of decision-makers in different organizations. It is difficult to respond to all customers’ demands because of increasing volume of them. On the other hand, in current competitive markets, ...
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Today evaluation of customers to classify the quality of providing services is one of the main challenges of decision-makers in different organizations. It is difficult to respond to all customers’ demands because of increasing volume of them. On the other hand, in current competitive markets, customers are considered as capital of organizations. This issue results in purposefully study on different groups of customers in competitive markets. One of the effective ways to study the customers and provide the optimism service to them is grouping the market and clustering the customers. In this research first customers classified in appropriate clusters using neural network techniques SOM in order to provide purposefully service , so each customer can deliver proper service according to its cluster. Then by the proposed model in the paper the membership of each client in the appropriate cluster can be predicted by using DEA-DA technique. This model has provided dynamic clustering process for organizations so that by which new customers will be assessed at any moment and their proper cluster is determined with reasonable accuracy.
M.J. Tarokh; K. Sharifiyan
Volume 6, Issue 17 , September 2007, , Pages 153-181
Abstract
Financial corporation and banks are sort of organization that due to specialty of their work, are very needy to customer management process ; and data mining is one of the best available tools for them to asses definition and behavior forecast of their customers.
Data mining is improving very fast and ...
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Financial corporation and banks are sort of organization that due to specialty of their work, are very needy to customer management process ; and data mining is one of the best available tools for them to asses definition and behavior forecast of their customers.
Data mining is improving very fast and due to presence of vide range of data using computer is essential. Nets & powerful algorithms are used to emplace of manual analysis to derive knowledge & information from data.
In this paper: “Mellat Bank” and its information bank of different division has been evaluated after data extraction from information bank and noise distortion , k means algorithm and fuzzy - k - means algorithm standard test of cluster's compression were used for customer clustering in groups. Determination of optimum number of clusters is done by applying cluster quality assay function. Afterward was used to determine the quality of gained clusters. Then the value of each cluster was determined through FRM model. At the end of project for clusters analysis and define appropriate strategy for each cluster; the pyramid of customer value was used.
