A Two-Phase Data Envelopment Analysis Model for Portfolio Selection

Abstract

When organizations do not have well defined goals and constraints, traditional mixed integer programming (MIP) models are ineffective for portfolio selection. In such cases, some organizations revert to building project portfolios based on data envelopment analysis (DEA) relative efficiency scores. However, implementing the k most efficient projects until resources are expended will not always result in the most efficient portfolio. This is because relative efficiency scores are not additive. Instead, the efficiency of each candidate portfolio must be evaluated against all possible portfolios, making for a computationally intensive task. This paper has two main contributions to the literature. First, we introduce a new DEA-MIP model which can identify the most efficient portfolio capable of meeting organizational goals at incremental resource levels. Second, by utilizing a second-stage DEA model to calculate the relative effectiveness of each most efficient portfolio, we provide managers, a tool for justifying budget increases or defending existing budget levels.

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