TETREES: Trade-off Evaluation Through Refined Exact Epsilon-Constraint Solver

TETREES is an open-source Python framework for multi-objective optimization that implements the exact ɛ -constraint method to compute complete and deterministic Pareto-optimal sets. Its modular architecture decouples objective definition, constraint modeling, and solver configuration, enabling reproducible workflows and seamless integration of new metrics or optimization modules. Built on Gurobi, TETREES supports fine-grained control over branching, heuristics, and search strategies, facilitating scalable exploration of complex decision spaces. A service-to-resource assignment case study illustrates how the framework exposes performance–value trade-offs and enables systematic evaluation of alternative solver configurations, providing a robust software environment for optimization research and decision-support tool development. • Modular design enabling flexible objectives, constraints, and solver control. • Fine-grained solver tuning for branching, heuristics, and search strategies. • Generates full Pareto sets while preserving original problem structure. • Validated on large-scale service–resource assignment with conflicting goals.