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In the context of project management, the attention given to project portfolio management has increased in recent years. The use of mathematical programming for portfolio management is also on the rise, because it integrates the project interactions with the multiple objectives of portfolio management into a single model. Among the possible objectives, recent studies have paid special attention to the emerging objective of future preparedness, which has not yet been incorporated into the existing mathematical models. This paper presents a mixed-integer nonlinear optimization model for portfolio selection that considers four main performance measures for project management, namely, value maximization, strategic alignment, balance, and future preparedness. Given the importance of the last measure, the purpose of this paper is to provide a more complete model that provides the marginal contribution and the best combination of projects according to the needs of the company. The model is tested using real data from two companies, one in Brazil and one in Canada, and the results obtained are coherent with their respective practices. [ABSTRACT FROM AUTHOR]

This paper puts forward an integrated model to support the process of classifying projects and project managers (PM) using a multiple criteria decision aid (MCDA) approach and allocating projects according to organizational restrictions through mathematical programming. The model was formulated after reviewing the literature and being guided by the findings of searches; we used a MCDA method for project and PM classification (first stage) and mathematical programming for project allocation (second stage). A practical application of the proposed model was implemented at a Brazilian electric energy company. The results demonstrated that it was possible to classify projects and project managers into definable categories, thereby enabling the process of project allocation to be undertaken more effectively. The project allocation process can be conducted in a systematic and more efficient way. The proposed model can support an organization by allocating its most critical projects to its best qualified and experienced professionals. [ABSTRACT FROM AUTHOR]