A Robust Optimization Approach to R&D Project Selection

Intense competition in the current business environment leads firms to focus on selecting the most appropriate R&D project portfolio in order to accomplish sustainable growth in the fierce market place. Achieving this goal is tied down by uncertainty which is inherent in all R&D projects. Therefore, investment decisions must be made within an optimization framework, based on the data which is usually unavailable or unreliable. In this paper, a model is developed to hedge against the R&D uncertainty. The proposed model is constructed by applying the concept of “real options”. The robust optimization approach is adopted to handle uncertain parameters and determine the optimal project portfolio. The problem is formulated as a robust zero-one integer programming model which is transformed into a standard mixed zero-one linear programming one and solved via an optimization technique. The applicability of the proposed approach is illustrated through an example.