  We present a review that unifies decision-support methods for exploring the
solutions produced by multi-objective optimization (MOO) algorithms. As MOO is
applied to solve diverse problems, approaches for analyzing the trade-offs
offered by MOO algorithms are scattered across fields. We provide an overview
of the advances on this topic, including methods for visualization, mining the
solution set, and uncertainty exploration as well as emerging research
directions, including interactivity, explainability, and ethics. We synthesize
these methods drawing from different fields of research to build a unified
approach, independent of the application. Our goals are to reduce the entry
barrier for researchers and practitioners on using MOO algorithms and to
provide novel research directions.
