Many-Objective Portfolio Optimization of Interdependent Projects with ‘a priori’ Incorporation of Decision-Maker Preferences

Abstract

Project portfolio selection is one of the most important problems faced by any organization. The decision process involves multiple conflicting criteria, and has been commonly addressed by implementing a two-phase procedure. The first step identifies the efficient solution set; the second step supports the decision maker in selecting only one portfolio solution from the efficient set. However, several recent studies show the advantages gained by optimizing towards a region of interest (according to the decision maker's preferences) instead of approximating the complete Pareto set. However, these works have not faced synergism and its variants, such as cannibalization and redundancy. In this paper we introduce a new approach called Non-Outranked Ant Colony Optimization, which optimizes interdependent project portfolios with a priori articulation of decision-maker preferences based on an outranking model. Several experimental tests show the advantages of our proposal over the two-phase approach, providing reasonable evidence of its potential for solving real-world high-scale problems with many objectives.

Portfolio Optimization From a Set of Preference Ordered Projects Using an Ant Colony Based Multi-objective Approach

“…15). Cruz et al 16 reported good results in problems with 9 and 16 objective functions working with an ant-colony based optimization method. Inspired in this works, we propose an ant colony algorithm, called Ant Colony Algorithm for Solving the Problem with the Ranking Information (ACO-SPRI), to solve Problem 6.…”

“…The selection scheme S i N' is based on the known roulette wheel technique, 16 with the probability of each project calculated as in Eq. (14).…”

“…The importance of such a search in a similar framework was analyzed by Cruz et al 16 This search explores regions near the best known solutions by a simple scheme consisting of selecting v projects at random, and generating all possible combinations of them for each solution. Small values for v provoke behavior that is too greedy, whereas large values produce intolerable computation times.…”

“…Algorithm Configuration. In order to set the ACO-SPRI algorithm parameters described in Section 4, we experimentally made small changes to the configuration given by Cruz et al 16 , paper that is a main reference on Ant Colony-based optimization of project portfolios. The tuning process involved exploring 256 1 2 , w and , which were selected as the most significant parameters by using a correlation analysis.…”

A multiobjective fuzzy model for selecting and planning a project portfolio in a public organisation

“…); thus, the multiobjective nature of the problem is patent (Mavrotas et al, 2008;Carazo et al, 2010;Ballestín and Blanco, 2011;Cruz et al, 2014).…”