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.

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.

Project portfolio management consists of repetitive cycles of Project evaluation، selection and execution. Project portfolio selection is the main part of Project portfolio management. Organization identifies and prioritizes Projects that are mostly aligned with stated strategic goals while considering real-world restrictions and considerations that are related to scheduling and resource allocation. In this paper، a new comprehensive model for the Project portfolio selection problem over a planning horizon with multiple periods by maximizing profit is developed in which simultaneous selection، scheduling and resource allocation of Projects are considered. The model incorporates Project interdependency and strategies of Project divisibility، reinvestment، external investment and resourcing in different ways at the same time in choosing the best execution schedule for the Projects in real-life applications. In addition، resource and budget constraints، cardinality restriction، precedence relationship and scheduling، setup and resource costs are included in the model. Numerical examples under eight scenarios are presented to highlight the characteristics of the proposed model.

Risk identification, impact assessment, and response planning constitute three building blocks of Project risk management. Correspondingly, three types of interactions could be envisioned between risks, between impacts of several risks on a portfolio component, and between several responses. While the interdependency of risks is a well-recognized issue, the other two types of interactions remain unacknowledged in the risk response planning literature. This research suggests a Bayesian belief network for modeling portfolio risks, their impacts, and responses. There are three kinds of nodes in this network: nodes representing portfolio risks, nodes corresponding to risk impacts on each objective of each portfolio component, and nodes showing response actions. The problem is to decide which responses are to be selected. For this purpose, an optimization model is proposed that minimizes the sum of both residual risk effects on portfolio component objectives and response implementation costs. Subsequently, a genetic algorithm is introduced to solve the model. A simple portfolio instance is also provided to illustrate the proposed model.

A vast number of organizational Projects as well as complexity of decision making process can cause particular challenges for Project management and its leadership. In order to use organization assets and opportunities efficiently, it is necessary that manager implement a comprehensive multidimensional Project portfolio management system that considers economic, social and technical details of the Projects. Resource constraint compels managers to select operational proposal Projects. Thus managers can maximize organizational utility due to Project portfolio’s resource constraint. This study considers the interactive effects of Project portfolio evaluation and sharing organizational Project resources with respect to its evaluation and choice of the Projects. In this two-step model, first a branch and bound algorithm with resource interaction was utilized to screen maximal portfolio and, in the next step, each portfolio was evaluated based on artificial neural networks to rank the end Project portfolios. Also, the ANN scores are strongly correlated with the DEA and COLS efficiency scores.

This paper deals with multi-period Project portfolio selection problem. In this problem, the available budget is invested onthe best portfolio of Projects in each period such that the net profit is maximized. We also consider more realisticassumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model ispresented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each timeperiod. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedureis presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solvethis type of problems. The GA is enhanced by a new solution representation and well selected operators. It also ishybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposedalgorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization(PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation resultsshow the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, theproposed algorithm is wisely combined with PSO to improve the computing time considerably.

Project portfolio selection problem has been investigated by many researchers over the decades. This paper presents a method for Project selection problem that is able to integrate the evaluation of individual Projects by considering their interactions on portfolio of company Projects. Moreover, since experts and their knowledge is one of the primary and valuable resources of an organization which evolves over time, the proposed system utilizes an arti, cial neural network approach to discover the experts' knowledge. This system has been used in a pilot organization in an organization while the output is close to the portfolio of Projects considered by the managers of the organization and has also contributed to the Project portfolio risk balancing. In order to validate the proposed method, an optimization model similar to the problem has been developed. The proposed approach has obtained similar results to the optimization and also needs much less time to solve large-sized problems using the proposed approach. This study tried to consider the interactions of Projects when they are selected simultaneously in the portfolio of Projects as well as the use of expert opinions and technical knowledge and experience of the organization in Project portfolio selection. The proposed approach has been implemented in a Project-oriented organization with varying levels of the number and complexity of the Projects that have achieved acceptable results. In order to validate the proposed approach, an optimization model is developed and implemented on three problems (two problems of the case study organization and one instance problem with a large number of Projects). The results showed acceptable similarity of results (nearly 90% similarity), but the time taken to solve the proposed approach is far less than the optimization model. The proposed approach is able to solve small, medium, and large problems while it provides timely and reasonable solutions in a reliable manner.