Application of genetic and differential evolution algorithms on selecting portfolios of projects with consideration of interactions and budgetary segmentation (Q2627549)

Summary: 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.