The complexity of modern agricultural systems requires resilient, adaptable supply chains to help achieve net-zero emissions. This study presents a two-stage stochastic programming model designed to address these challenges in a sustainable forward-reverse hybrid agricultural supply chain (ASC).
The proposed methodology integrates sustainability, resilience and circular economy (CE) principles into a two-stage stochastic optimization model for ASC. The model includes waste valorization (WV) by recycling agricultural waste in composting centers, turning it into valuable resources. It also incorporates resilience strategies, including an inventory buffering policy and a multi-level energy fortification strategy integrating gas, solar panels and gasoline, within a mixed-integer linear programming framework. The model is solved using the General Algebraic Modeling System with the CPLEX solver. A real-world hydroponic agriculture case study and test problems validate the model, with sensitivity analysis to evaluate the impact of key parameters.
Integrating clean energy, resource efficiency and waste reuse supports the global goal of net-zero emissions in agriculture. This approach reduces raw material purchasing costs by 24%, enhancing sustainability, while increasing profitability by 15%. It provides a practical, scalable pathway to a low-carbon ASC.
This study integrates CE principles with stochastic optimization to design a low-carbon, adaptable ASC. A hybrid greenhouse–field combining hydroponic farming and traditional agriculture optimizes resource efficiency, WV and clean energy use, ensuring sustainability and profitability.
