<p>Multiple projects are generally executed concurrently in an organization. This study discusses selecting an optimal subset of projects from a set of candidate projects to maximize benefits under limited resources. The mathematical model considers project interdependency and cardinality constraints. It is challenging to solve for a globally optimal solution. Heuristic methods cannot guarantee the global optimality of the obtained solution, and exact methods with complicated model transformations are inefficient. This study utilizes a novel linearization approach to efficiently transform the project portfolio selection problem with pairwise and three-way cross-product terms as a mixed-integer linear program. Compared with current transformation methods, the proposed method reduces numerous continuous variables required to linearize the original problem and significantly enhances the computational efficiency. Additionally, the effectiveness and practicability of the proposed method are demonstrated using data from a semiconductor company. The principles of project portfolio selection, such as optimizing resource allocation, managing interdependencies, and maximizing benefits, are applicable across various industries. The proposed method can help organizations in different sectors efficiently select an optimal project portfolio under limited resources.</p>