Nowadays, organizations are faced with a multitude of PROJECT and investment opportunities. Despite the importance of various criteria, complexity of multi-objective models and weakness of OPTIMIZATION algorithms often compelled manager to limit the selection criteria or only suffice to financial objects. In this paper, it is endeavored to extend selection criteria by using an efficient OPTIMIZATION algorithm based on teaching-learning process, which makes it possible to solve the proposed 0-1 multi-objective programing model. Finally efficiency of the applied algorithm called TLBO is compared with PSO and GA by applying the proposed model in a PROJECT oriented organization. It was shown that TLBO is better than GA and PSO algorithm technique, which was used before in such problems.

Existing PROJECT selection models do not consider the complexity of PROJECTs as a selection criterion, while their complexity may prolong the PROJECT duration and even result in its failure. In addition, existing models cannot formulate the aggregate complexity of the selected PROJECTs. The aggregated complexity is not always equal to summation of complexity of PROJECTs because of possible synergies or conflicts between them may increase or decrease the total complexity. In this paper, a model is proposed for measuring the aggregate complexity in the selection of PROJECT PORTFOLIOs. A case study is presented to show the usefulness of the model and its applicability in practice. Moreover, several large-sized numerical examples have been tested showing the capability of the model to solve such problems in logical computational time.

worldwide rivalry of commerce surroundings lead organizations to focus on selecting the best PROJECT PORTFOLIO among available PROJECTs utilizing their scarce resources in the most effective manner. To accomplish that, organizations should consider the uncertainty intrinsic in PROJECTs on the basis of an appropriate valuation techniques with regard to flexibility in investment decisions making within an OPTIMIZATION framework. In this research, the problem of PROJECT selection under uncertain environment is formulated by using robust OPTIMIZATION model for dealing with the complexities and uncertainties regarding the construction of a PROJECT PORTFOLIO. First of all, a general mathematical formulation which can address compound real option valuation is presented which accounts for managerial flexibility, is employed to correct the deficiency of traditional discounted cash flow valuation that excludes any form of flexibility. Then, A PROJECT selection model is presented by robust OPTIMIZATION, which are effective for solving problems under uncertainty. Finally, by supposing budget of the organization to be rare, the model maximize the n_fold compound options formula as the objective function solve according to combinatorial robust OPTIMIZATION algorithm. An example is provided to illustrate the proposed decision approach.

Introduction: Growing influence of the PROJECT as a way to organize things in many organizations require effective management of multiple PROJECTs.. Due to the complexity of IT PROJECTs in organizations due to resource constraints especially if you have multiple PROJECTs, the necessity and use of PROJECT PORTFOLIO management in achieving PROJECT objectives in recent years much attention has been. In this research, PORTFOLIO management of IT PROJECTs in Isfahan University of Medical Sciences IT PROJECT PORTFOLIO Management at University level, based on a three-step model Bert De Reyck (1 - Inventory PORTFOLIO 2- PORTFOLIO OPTIMIZATION 3 - Managing PORTFOLIO) were investigated.Methods: the present study according to the aim is applied and according to the method of data collection is descriptive - survey Subset of Athletics -. Validity has been confirmed by a number of experts and professors and reliability using Alpha Cronbach coefficient was calculated to 82 Percent. Statistical Society, University ICT All employees that have been done in 1392 AP and to analyze the results of descriptive statistics - mean and standard deviation and - Univariate t-test and a significance level of 0/05 to SPSS 18 software is used.Results: Descriptive analysis of the components of PROJECT PORTFOLIO management based on the three organized model Bert De Reyck indicated that the first phase had a mean 2.45±0.81, the second stage has a mean of 2.02±0.69 and The third phase has a mean of 1.87±0.74 respectively. Effects of PORTFOLIO management on the effectiveness indicate that the average effectiveness of PROJECTs in each of their indices optimal level and average has been over the 2.75. (P-Value<0.05) And therefore effectiveness is in Positive relation to PORTFOLIO management. The mean level of problems in each of its PROJECTs under PORTFOLIO management indicated that average was more than 2.75. (P-Value<0.05) And therefore problems are in negative relation to PORTFOLIO management.Conclusion: University study showed that the level of IT PROJECT management is in the second stage of Bert De Reyck model. Also Under one PROJECT PORTFOLIO Management can add on the effectiveness of PROJECTs and finally a review of the problems of PORTFOLIO management PROJECT PORTFOLIO management indicated that the level is lower than are added to the institutional PROJECT problems.

‎‎The PORTFOLIO OPTIMIZATION problem, including PORTFOLIO selection, typically aims to maximize return and minimize risk. In this paper, we discuss about increasing use of stochastic PORTFOLIOs in investments and aim to create optimal PORTFOLIOs. It follows the relative wealth process of these PORTFOLIOs, outperforms the market PORTFOLIO over sufficiently long time-horizons. In this regard, initially, a model of the market is presented by the stochastic PORTFOLIO theory (SPT) and features like Growth rate, Excess growth rate are mentioned. Then, functionally-generated PORTFOLIOs are defined by using diversity weighted PORTFOLIOs with parameters p ∈ (0, 1), p < 0 and combination of them. Finally, by obtaining the daily closing price of 10 stocks in Tehran Stock Exchange (TSE) ,the performance of diversity weighted PORTFOLIOs is investigated.

The Markowitz’s OPTIMIZATION problem is considered as a standard quadratic programming problem that has exact mathematical solutions. Considering real world limits and conditions، the PORTFOLIO OPTIMIZATION problem is a mixed quadratic and integer programming problem for which efficient algorithms do not exist. Therefore، the use of meta-heuristic methods such as neural networks and evolutionary algorithms has been an important issue in the literature of PORTFOLIO OPTIMIZATION. This study considers the problem of finding the efficient frontier associated with the standard mean-variance PORTFOLIO OPTIMIZATION model and presents a heuristic algorithm based upon particle swarm OPTIMIZATION for finding the cardinality constrained efficient frontier. The test data set is the daily prices of 20 companies from March 2006 to September 2008 from the TEPIX in Iran. The results show that PSO is successful in constrained PORTFOLIO OPTIMIZATION to find the optimum solutions in all levels of risk and return.

In optimizing the PORTFOLIO, the main issue is the optimal selection of assets that can be bought with a certain amount of money. Although risk minimizing and revenue maximizing on investment seems simple, but in practice several approaches have been proposed for an optimal PORTFOLIO. In 1950, Harry Marquitz introduced his model in which proposed the OPTIMIZATION of the asset basket as a quadratic programing model with the aim of minimizing the variance of the asset set, provided that the expected return equals a constant value. In this research, the problem of three-objective OPTIMIZATION (i. e., maximizing stock returns, minimizing its risk and the third objective function, namely minimizing the number of assets) has been studied. Accordingly, investors, with admission a small amount of risk and a similar amount of return, will choose a basket of less assets. For this purpose, at first, genetic algorithms and multi-Particle Swarm OPTIMIZATION algorithm were used to estimate the two-objective model of minimum variance and maximum return for better algorithm identification. Then, with regard to the better performance of the algorithm, this algorithm was used to estimate the three-objective model for maximizing stock returns, minimizing risk, and minimizing the number of assets.