A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection

Project selection is a complex decision making process that is influenced by multiple and often conflicting objectives. The complexity of the project selection problem is due to the high number of projects from which a subset (portfolio) has to be chosen. We present a hybrid fuzzy rule-based multi-objective framework for sustainable project portfolio selection. The multiple and conflicting objectives are considered as the input variables in a Fuzzy Rule-Based (FRB) system developed to estimate the overall fitness (suitability) of the potential project portfolios. A hybrid multi-objective framework integrates and synthesizes the results from a data mining model with the results from a Data Envelope Analysis (DEA) model and an Evolutionary Algorithm (EA) to design the structure of the proposed FRB system. The proposed framework simultaneously considers the accuracy maximization and the complexity minimization objectives. A Genetic Based Machine Learning (GBML) method is utilized to design an alternative FRB system for comparison purposes. The proposed framework and the GBML method are used to assess the alternative project portfolios in a real-world financial services institution. The statistical analysis shows the performance dominance of the proposed hybrid framework over the GBML method based on selected accuracy and interoperability measures.

► Project portfolio selection is a complex multi-criteria problem.
► A Fuzzy Rule-Based (FRB) system is used to estimate the fitness of portfolios.
► Data mining, DEA and evolutionary algorithm are used to design the FRB system.
► Accuracy maximization and complexity minimization are considered simultaneously.
► Selected accuracy and interoperability measures are used to assess the framework.