Pharmaceutical R&D Portfolio Optimization with Minimum Borrowed Capital Based on Fuzzy Set Theory

Due to long lead times, uncertain outcomes and lack of enough historical data, pharmaceutical research and development (R$\&$D) portfolio selection is a often very complex decision issue. The aim of this paper is to investigate pharmaceutical R$\&$D portfolio selection with unavailable and unreliable project information, where the borrowed capital is allowed. Based on fuzzy set theory, we propose two pharmaceutical R$\&$D portfolio optimization models with minimum borrowed capital by taking into account corporate strategy in developing new products, scarcity of resources, lack of investment budget and cardinality constraint. In the two proposed models, the pharmaceutical R$\&$D company is assumed to achieve the objectives of maximizing terminal wealth and minimizing the cumulative borrowed capital over the whole investment horizon. Then, we transform the two proposed bi-objective models into the corresponding single-objective models by using the weighted sum approach and employ the modified artificial bee colony (ABC) algorithm to solve the transformed models. Finally, we provide a numerical example to illustrate the application of the proposed models.