Hybrid metaheuristic approach for handling many objectives and decisions on 
partial support in project portfolio optimisation

Abstract

Organisations often face portfolio optimisation problems. In many practical cases, the 
decision is not only to select a subset of applicant projects but also to assign a number 
of resources to the favoured proposals within the projects' feasibilities. In the scientific 
literature, multi-objective optimisation algorithms have addressed this issue by means of 
generating redundant dummy projects for each proposal that is likely to be partially funded. 
However, unfortunately, if there are many projects that can be supported in a wide variety 
of ways, this approximation results in a large overload for the optimisation methods, which 
provokes poor algorithmic performance. To alleviate these problems, we propose the Non-Outranked 
Ant Colony Optimisation II method, which incorporates a fuzzy outranking preference model for 
optimising portfolio problems with partial-support features. The advantages offered by this 
novel approach are supported by a series of experiments that provide evidence of its capacity 
for solving those real-world problems in which the level of resources allocated to the selected 
proposals has a proportional impact on the projects' expected benefits.