Triple-bottom-line optimization of waste-to-biodiesel supply chains in India: Multi-objective decision support via social group optimization

This study proposes a multi-objective optimization framework for a biodiesel supply chain in India, integrating cost, emissions, and employment as conflicting objectives. The model considers heterogeneous waste feedstocks – animal fat, municipal solid, and industrial organic waste – and is solved using the Social Group Optimization (SGO) algorithm, a novel metaheuristic inspired by human social dynamics. The model incorporates India-specific data, infrastructure constraints, and co-product glycerol flows to enhance real-world applicability. The generated Pareto-optimal solutions demonstrate significant trade-offs: the best balanced solution achieves a total cost of 1.57 million USD, 43.7 metric tons of CO 2 -equivalent emissions, and 680 jobs created; an employment-maximizing configuration yields 964 jobs with 49.8 emissions and a cost of 2.35 million USD. Sensitivity analyses reveal the influence of transport and infrastructure costs on decentralization and sustainability outcomes. The results highlight that small increases in cost can enable large social and environmental benefits. By operationalizing triple-bottom-line sustainability through a mathematically rigorous, data-driven approach, this study offers a robust decision-support tool for policymakers seeking to expand India’s waste-to-biodiesel infrastructure. The framework contributes to national energy targets and provides a scalable model for circular bioeconomy planning in emerging economies. • A triple-bottom-line optimization framework is developed for biodiesel supply chains. • Social Group Optimization is applied to solve conflicting sustainability objectives. • The model is tailored to India’s waste-based biodiesel supply chain conditions. • Decentralized facility networks reduce emissions and enhance employment. • Cost-emission-employment trade-offs provide policy-relevant insights.