Multi-objective optimization framework for sustainable olive oil supply chains: integrating environmental compliance with economic performance

Introduction Mediterranean olive oil production faces a critical sustainability challenge: managing olive mill wastewater (OMW), a highly polluting byproduct (0.8–1.2 m 3 /ton) threatening environmental compliance and supply chain viability. Methods This study develops an integrated multi-objective Mixed-Integer Linear Programming framework that jointly optimizes harvest scheduling, processing allocation, and OMW management. The ε-constraint method generates Pareto frontiers revealing trade–offs between environmental performance, oil quality, and economic profit. Implementation used Python 3.9+ with Gurobi optimizer, validated through a Tunisian case study (18 groves, 4 teams, 3 mills, 14-day horizon). Results Operational optimization achieved 14.6% CO 2 emission reduction and maintained regulatory compliance. However, OMW valorization remained at 12.8%-far below 80% policy targets-revealing fundamental infrastructure-regulation disconnect. Multi-objective analysis shows balanced solutions achieve 80–85% of maximum performance across all dimensions, while single-objective approaches sacrifice 35–65% in non-prioritized objectives. Sensitivity analysis quantifies that doubling valorization capacity increases recovery to 23.6% with 30.8% profit improvement. Discussion The framework demonstrates that environmental constraints, when architecturally embedded, define opportunity spaces for competitive advantage rather than performance limitations. Infrastructure analysis provides actionable guidance for phased regulatory implementation with 3–4 season payback periods.