Organizations continuously increase their efforts to improve their project portfolio management (PPM). As a consequence, software that supports the processes of PPM is a standard application in organizations. Common PPM software provides various methods to support the decision process, collecting informative data to do so. The lack of data was in the past the main reason for the failure of mathematical optimization methods in the area of PPM. Thus, the connection of mathematical optimization methods with frequently used PPM software seems to be a promising approach to enhance the applicability of mathematical optimization methods in the area of PPM.
Therefore, the first part of this work outlines how a mathematical optimization model must be designed so that it can be embedded into existing PPM software. Based on these requirements, a mathematical optimization model is formulated. Thereby, the main focus lies on incomplete information as well as on optimal budget allocation among strategic buckets. Incomplete information refers to vaguely formulated project parameters due to prediction difficulties. To process vaguely formulated parameters, robust optimization concepts are used. In the context of strategic buckets, we discuss the divisibility of the entire portfolio into subportfolios so that every strategic bucket is represented by a subportfolio. The main goal of strategic buckets is to enforce a certain budget allocation among projects to implement the desired strategy. To support the allocation of the budget among strategic buckets, we define their marginal values and use those values as decision criteria.