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.

Purpose: The Project portfolio problem proceeds to choose a subset among the proposed Projects in organization. One of the purposes of Project portfolio management is the reduction of the available risks in portfolio selection process. In real world, the Project risks are rarely independent, and generally have a degree of interaction. By considering this interaction the more accurate evaluation of portfolio selection can be achieved. The purpose of this study is to provide a framework for selecting Project portfolios by considering the triple interaction between Projects for the first time using the correlation coefficient and the theory of cross-information for the risk factors affecting them. Design/methodology/approach: First, this research identifies the most critical risks based on the literature review, Project management body of knowledge and the relevant experts' opinions. Then, a questionnaire was distributed among 30 experts to quantify the identified risk criteria. The identified risks that are included 11 risks are in the categories of “ technical” , “ external” , “ organizational” and “ Project management” . Then theses risks are placed in to the “ cost” , “ time” and “ quality” classification. By considering the individual Project risk, the Projects which their risks were higher than the allowable limit, were not able to compete in the portfolio. Also, the pairwise and triple-wise interaction of the Projects based on effective criteria caused that the Projects which provoke risks synergies in the portfolio to be prevented from being together. By analyzing risks, the pairwise interaction effects between Projects are calculated using Spearman's correlation coefficient and for the first time the triple interaction effects between Projects are calculated using theory of crossinformation to discover more precisely the effect of factors on each other. Finally, the modeling in the form of a case study based on integer linear programming along with two goals, “ maximize IRR” and “ maximize the number of Projects in the portfolio” , are accurately assessed to evaluate the research validity. Findings: The problem of Project portfolio is the selection of a subset of proposed Projects according to the strategic goals of the organization and the associated constraints. This paper provides a framework for selecting Project portfolios by considering the triple interaction between Projects for the first time. The results show that considering pairwise and triple-wise interaction instead of individual pairwise interaction comprise different answers. This result leads to discovering the relationship which can not be identified just with the pairwise interaction. Research limitations/implications: In this research the risk criteria are considered generally, while, in specific Projects it can be used specific criteria. Using the meta-heuristic methods when the problem dimensions are increased is another area for future research. In addition, fuzzy methods and stochastic subject in order to quantify the risk criteria could be considered. Besides, this paper considers risks with negative aspect for Project portfolio selection, while, in future research suggested that the risks with aspect of the positive are considered as well. Practical implications: One of the most important application of this paper is for Project-oriented companies that are involved in doing multiple Projects simultaneously. The results of this paper are useful for these companies for risk reduction in selecting and performing their Projects. Social implications: This paper considers the most important risks affecting Project execution. These risks include external risks of organization as well as internal and technical risks of organization. So, the paper helps organizations for the most appropriate selection of Projects that could have benefits for organization and also society. Originality/value: In the literature, there are various methods that researchers have considered for risk management. Most of these studies don’ t consider interactions between Projects or simply they considered only pairwise interaction. The main purpose of this study is to consider a higher level of interactions that is triple interactions that not considered in the literature. The results show that considering triple-wise interaction instead of individual pairwise interaction comprise different and better results.

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.