  In portfolio optimization, decision makers face difficulties from
uncertainties inherent in real-world scenarios. These uncertainties
significantly influence portfolio outcomes in both classical and
multi-objective Markowitz models. To address these challenges, our research
explores the power of robust multi-objective optimization. Since portfolio
managers frequently measure their solutions against benchmarks, we enhance the
multi-objective min-regret robustness concept by incorporating these benchmark
comparisons.
  This approach bridges the gap between theoretical models and real-world
investment scenarios, offering portfolio managers more reliable and adaptable
strategies for navigating market uncertainties. Our framework provides a more
nuanced and practical approach to portfolio optimization under real-world
conditions.
