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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]

In "Operations Research: Topics, Impact and Trends from 1952–2019," A. Calma, W. Ho, L. Shao, and H. Li retrospectively look at 68 years of publication of the Operations Research. Using 5,440 journal articles, they highlight the top contributing countries and authors and top research methods and problems investigated. Mathematical programming is the most common research method, whereas inventory is the most investigated problem. Investigations related to pricing are growing significantly. The United States, Canada, and the United Kingdom publish the most papers, with the United States and Canada having similar publication profiles per capita. Inventory is the most popular research problem studied by North American, Asian, and Middle Eastern countries, whereas European countries focus on scheduling problems. Network visualizations of the journal's last 10 years show dynamic programming as the most used method and pricing as the most studied problem. Coauthor networks on collaborations on both dynamic programming and pricing are also shown. This paper is a retrospective look at 68 years of publication output of Operations Research, revealing changes in its publications, its authors, and their impact over time and how these changes might affect researchers and practitioners in the present. A total of 5,440 journal articles from its inception in 1952 to 2019 are used. The analysis initially focuses on the most studied topics and then continues with the top research methods and research problems investigated. The top contributing countries and authors to the most investigated research problems are also studied. The results indicate that mathematical programming is the most common research method, whereas inventory is the most investigated problem. However, investigations related to pricing are growing significantly. The United States, Canada, and the United Kingdom publish the most papers, with the United States and Canada having similar publication profiles per capita. Inventory is the most popular research problem studied by North American, Asian, and Middle Eastern countries, whereas European countries focus on scheduling problems. In order to understand the latest research trend, we visualize the networks of the last 10 years of Operations Research that show dynamic programming as the most used method and pricing as the most studied problem. We further visualize the coauthor networks on both dynamic programming and pricing to identify the most significant clusters of researchers and the topics these research clusters collaborate on. Finally, we provide researchers information about where Operations Research is and where it is likely heading. [ABSTRACT FROM AUTHOR]