AUTHOR=Casanova Carlos , Schab Esteban , Prado Lucas , Rottoli Giovanni Daián 

TITLE=Hierarchical clustering-based framework for a posteriori exploration of Pareto fronts: application on the bi-objective next release problem

JOURNAL=Frontiers in Computer Science

VOLUME=Volume 5 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2023.1179059

DOI=10.3389/fcomp.2023.1179059

ISSN=2624-9898

ABSTRACT=When solving multi-objective combinatorial optimization problems using a search algorithm without a priori information, the result is a Pareto front. However, selecting a solution from it is a laborious task that can benefit from a systematic approach. This paper proposes an abstract framework based on hierarchical clustering in order to facilitate DMs to explore such a Pareto front in search of a solution or a group of solutions according to their preferences. An extension of that abstract framework aimed at addressing the bi-objective Next Release Problem is presented, together with a Dashboard that implements that extension. The results of the initial empirical usability study using a small group of experts are promising and indicate directions for future improvements. The experts were able to correctly use the dashboard and properly interpret the visualizations in a very short time.