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.

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.

The aim of this study is to present an efficient solving method for the Project portfolio selection problem. The objective is to maximize the net present value (NPV) of the Project portfolio. The problem is first modeled mathematically. Then, two meta heuristics, the genetic algorithm and simulated annealing, are applied to solve this NP-hard problem. Finally, a comprehensive computational experiment is performed on a set of instances. The results of the computational experiment show that the genetic algorithm performs better than the simulated annealing algorithm.

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.

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.

In most Project driven organizations, portfolio management is integrated with Project management to select Projects in accordance with the strategies of the organization and its product innovation.This research initially refines the standard selection criteria through interviews with organization mangers and experts of the Iranian Building Investment Holding. These refined criteria were further prioritized based on field questionnaires and used as the basis for selecting the Project portfolio in this Holding. The results indicate that the selected Project portfolio was not always in line with organization strategies. The main reasons for this discrepancy include diversified holding activities, unmonitored expansion of the portfolio, and ad hoc decision making in response to the volatile economic environment.

This paper investigates the problem of selecting and scheduling a set of Projects among available Projects. Each Project consists of several tasks and to perform each one some resource is required. The objective is to maximize total benefit. The paper constructs a mathematical formulation in form of mixed integer linear programming model. Three effective metaheuristics in form of the imperialist competitive algorithm, simulated annealing and genetic algorithm are developed to solve such a hard problem. The proposed algorithms employ advanced operators. The performance of the proposed algorithms is numerically evaluated. The results show the high performance of the imperialist competitive algorithm outperforms the other algorithms.

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.