Application of genetic and differential evolution algorithms on selecting portfolios of projects with consideration of interactions and budgetary segmentation

Nowadays, defining new projects is significantly vital and necessary for many organisations and companies. The problem arise here is how to select an appropriate portfolio from a set of candidate projects. A good combination of projects can extensively promote the organisations in their competitive performance. Thus, the purpose of this study is to present a practical model in addition to some solution approaches to choose the best and proper project portfolios with the considerations of projects' interactions, quantitative and qualitative criteria, and practical constraints. A linear formulation has been proposed which considers the interaction effects and integrates the number of selected projects, the segmentations, and the budgetary constraints into a single set of constraints. In order to solve the proposed model, a genetic algorithm and also a differential evolution algorithm are presented. Moreover, the efficiencies of these two algorithms are compared with an exact method using various numerical examples. Finally, through a case study the performance of the model is demonstrated.