Optimal project portfolio selection with carryover constraint

• Published in: The Journal of the Operational Research Society Vol. 60; no. 12; pp. 1649 - 1657
• Main Author: Gurgur, C
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 01.12.2009; Palgrave Macmillan; Palgrave Macmillan UK; Taylor & Francis Ltd
• Subjects: Applied sciences; Budget constraints; Budgets; Business and Management; Business risks; Business structures; Capital budgeting; Capital investments; Case-Oriented Paper; Case-Oriented Papers; Commercial regulation; Decision analysis; Decision theory; Decision theory. Utility theory; Exact sciences and technology; Expenditures; Financial portfolios; Firm modelling; Gaussian distributions; Hierarchies; Linear programming; Management; Mathematical programming; multi-attribute utility theory; Operational research and scientific management; Operational research. Management science; Operations research; Operations Research/Decision Theory; Optimization; Portfolio management; Random variables; Rationing; stochastic programming; Studies; Valuation; multi-attribute utility theory; capital budgeting; stochastic programming; decision analysis; Project management; Capital; Project selection; Decision analysis; Stochastic programming; Return on investment; Portfolio selection; Utility theory; Budget; Value analysis; Portfolio management; Investment; Firm strategy; Completion time
• ISSN: 0160-5682; 1476-9360
• DOI: 10.1057/jors.2008.104

In a typical capital rationing problem, a project portfolio is selected to maximize expected return on investment while adhering to the capital budget constraint. Sometimes projects may be delayed and they have to be funded beyond their planned completion time. This type of 'unplanned carryovers' represents a financial obligation to the company. If future years' capital budgets cannot be expanded to cover such obligations, future projects may be cancelled or postponed to fund the unplanned carryover. In this paper, we develop a methodology based on multi-attribute utility theory and chance-constrained programming to optimize portfolio selection subject to the constraints that the selected portfolio does not exceed the available budget and that the carryover of the unspent funds to the next fiscal year does not exceed predetermined limits. We use this technique to select an optimal project portfolio for Lockheed Martin Space Systems' infrastructure investments.