cuOpt Multi-Objective Exploration enables developers and operations researchers to navigate complex mathematical tradeoffs by orchestrating repeated single-objective cuOpt LP, MILP, and QP solves into a comprehensive Pareto frontier. Instead of arbitrarily collapsing conflicting business targets into a single weighted score, this skill systematically applies weighted-sum and ε-constraint scalarizations, establishes payoff tables for normalized scaling, filters dominated solutions, and intelligently refines search grids where tradeoffs are steepest. It is ideal for operational scenarios like balancing transportation cost against customer service levels or financial return against portfolio risk without losing visibility into non-convex tradeoffs.