As global population growth accelerates, demand for agricultural products has surged, leading to higher production, rising costs, increased water use, and food shortages. This study proposes a sustainable agricultural supply chain network that prioritizes water conservation while meeting customer needs. A mathematical model optimizes a closed-loop supply chain, maximizing demand for agricultural products and compost. The model minimizes costs, maximizes customer satisfaction, and reduces water consumption, ensuring sustainability. A stochastic programming approach manages supply and demand uncertainties through scenarios. Results show that increasing customer satisfaction raises costs and water use. For example, increasing the customer importance factor from 0.2 to 0.8 increases total costs by 4.53% and water use by 43.75%, highlighting the sensitivity of water use to customer satisfaction. Reducing processing center capacity decreases water use but increases costs and reduces customer satisfaction. A 50% reduction in capacity raises costs by 56.41%, decreases customer satisfaction by 4.44%, and reduces water use. Water use reductions vary by stage: a 50% reduction in agricultural production cuts total water use by 32.33%, while similar reductions in processing and composting yield smaller decreases of 17.86% and 28.32%, respectively. This underscores agricultural production as the most water-intensive phase. The model’s effectiveness is demonstrated through numerical examples and sensitivity analyses. Metrics such as the Number of Pareto Fronts (NPF) and Maximum Spread Index (MSI) are used to compare solutions. This study emphasizes aligning sustainable production, resource conservation, and customer needs to create a resilient agricultural supply chain.
