Multi-objective binary cuckoo search for constrained project portfolio selection under uncertainty

Abstract

One of the recurrent complex decisions faced by organisations is project portfolio selection (PPS) in which a group of the most beneficial projects must be selected from a set of candidate projects. Accordingly, effective means for selecting projects must be employed in order for the organisation to survive in today's extremely competitive business environments. This paper proposes a framework that integrates a multi-objective binary cuckoo search (MOCS) algorithm with Monte Carlo simulation to help decision makers select the best portfolio based on a given set of parameters (criteria and performance values) and several characteristics (uncertainty, constraints and multi-objective). Performance of the proposed framework was measured against a published research on two cases: 1) when transforming all objectives into one, the proposed algorithm outperformed recent algorithms used to solve the problem on a large-scale where these algorithms fail to reach an optimal solution; 2) when dealing with a multi-objective case, the proposed algorithm outperformed reported earlier results with 90% of the non-dominated solutions were obtained by it. [Received 30 September 2016; Revised 29 January 2017; Revised 24 May 2017; Revised 11 August 2017; Accepted 25 August 2017]