The project selection problem is considered as one of the most imperative decisions for investor
organizations. Due to non-deterministic nature of some criteria in the real world projects in this paper, a new
model for project selection problem is proposed in which some parameters are assumed probabilistic. This
model is formulated as a non-linear, multi-objective, multi-period, zero-one programming model. Then the
epsilon constraint method and an algorithm are applied to check the Pareto front and to find optimal solutions.
A case study is conducted to illustrate the applicability and effectiveness of the approach, with the results
presented and analysed. Since the proposed model is more compatible with real world problems, the results
are more tangible and trustable compared with deterministic cases. Implications of the proposed approach are
discussed and suggestions for further work are outlined
