Data and Code for "Exact ε-Constraint Optimization for Spatial Planning: Balancing Species Risk Distribution in Ecological Restoration"

The repository implements an exact multi-objective optimization framework for ecological restoration planning based on mixed-integer linear programming (MILP), piecewise-linear (PWL) approximations, and the ε-constraint method. The framework is designed to explore trade-offs between aggregate extinction risk reduction and the equitable distribution of species risk outcomes across restoration portfolios. The optimization workflow combines: Exact ε-constraint multi-objective optimization; Minimax and efficiency-oriented formulations; Piecewise-linear approximations of nonlinear extinction risk functions; Spatially explicit restoration prioritization under area budget constraints. The repository includes: Python source code for model construction and optimization; Input datasets and preprocessing scripts; Optimization outputs and Pareto-frontier portfolios; The optimization models were implemented in Python using Gurobi, NumPy, and Pandas. This repository is intended to support transparency, reproducibility, and future methodological extensions in ecological restoration and spatial planning.