Multi-objective uncertain project selection considering synergy

This paper discusses a multi-objective mean-variance model and its solution algorithms for the project selection considering synergy under the uncertain environment. Two objective functions have been considered: maximizing the expected net present value (NPV) of the selected projects and minimizing the risk measured by variance of NPV. Here, the profits and investment outlays for candidate projects and synergistic profits and outlays of interdependent projects are considered as uncertain variables whose distributions are determined by experts’ evaluations. According to uncertainty theory, the deterministic equivalents are obtained. The effect of uncertainty on project selection is analyzed through comparison between the proposed uncertain model and the certain model with exact parameters. And the effect of synergy on the project selection is also analyzed. To get the Pareto-optimal solutions of the proposed multi-objective project selection model, we provide a new multi-objective modified binary Jaya (MOMB-Jaya) algorithm and a new multi-objective modified binary Rao (MOMB-Rao) algorithm, which respectively are modifications of the Jaya and Rao algorithms for solving the proposed multi-objective problems. Through numerical experiments on 15 example problems, including large-scale problems, the performances of the proposed multi-objective binary algorithms are tested. Comparison with the binary version of non-dominated teaching-learning-based optimization (NSTLBO) algorithm shows the better performance of the MOMB-Rao algorithm. Finally, a numerical example is given to demonstrate the validity of the proposed multi-objective uncertain model.