Selecting a portfolio of projects considering both optimization and balance of sub-portfolios

• Main Authors: Nima Golghamat Raad, Mohsen Akbarpour Shirazi, S.H. Ghodsypour
• Format: Article
• Language: English
• Published: Growing Science 2020-01-01
• Series: Journal of Project Management
• Subjects: Project Portfolio Selection; Prioritization; Clustering; Neural Network; FAHP; Multiobjective Programming
• Online Access: http://www.growingscience.com/jpm/Vol5/jpm_2019_24.pdf

Over the past four decades, portfolio selection has been one of the most important con-cerns of researchers, project managers, project-oriented companies, and public agencies around the world. Although numerous studies have been done in this field, still there is a room for more improvement in both theory and practice. One of the yet unspoiled topics in this field is improving and balancing the efficiency of sub-portfolios while paying attention to portfolio optimization. This study employs data-mining tools to categorize projects into sub-portfolios and rank them. Multiple Criteria Decision Making (MCDM) methods are also used to weigh the criteria on which the ranking process is based. Finally, a novel multi-objective model is designed to optimize the efficiency of sub-portfolios and the gain of the main portfolio. The model is solved by NSGA II algorithm. This study introduces a hybrid framework by which project portfolio selection process can be carried out regarding strategic alignment, cost, and risk.