Integrated Decision-Making in Sustainable Closed-Loop Dual-Channel Food Supply Chains

The increasing adoption of dual sales channels and the growing emphasis on sustainability have made decision-making in food supply chains considerably more complex. In addition, the Food Standards Agency’s authorization of simultaneous human food and animal feed production has created new opportunities for integrating food-waste recovery and circular-economy practices within food supply chains. Despite extensive research on dual-channel and sustainable supply chains, relatively little attention has been devoted to the simultaneous optimization of pricing, facility location, inventory control, routing, and food-waste recycling decisions within a closed-loop supply chain framework. To address this gap, this study develops a novel mixed-integer nonlinear programming model for a multi-product, multi-echelon, sustainable closed-loop dual-channel food supply chain. The proposed framework integrates strategic, tactical, and operational decisions while considering economic, environmental, and social sustainability objectives. This study contributes to the literature by jointly optimizing pricing, facility location, inventory control, routing, and food-waste recycling decisions while incorporating the conversion of food waste into raw materials for animal feed production. To solve large-scale instances, three hybrid multi-objective metaheuristic algorithms are developed by integrating NSGA-II with the Artificial Jellyfish Search (AJS), the Forensic-Based Investigation (FBI), and the Whale Optimization Algorithm (WOA). From a business-analytics perspective, the proposed framework functions as a prescriptive decision-support approach that converts supply chain data into coordinated strategic, tactical, and operational decisions and provides a foundation for integration with data-driven and AI-enabled analytics systems. Computational experiments demonstrate that AJS-NSGA-II outperforms the competing hybrid algorithms in four of the six evaluation metrics. Sensitivity analysis indicates that increasing direct-channel adoption from 28% to 36% enhances both economic and environmental performance, whereas a shift toward the indirect channel can reduce profit by up to 7.6%. Furthermore, increasing the food-waste recovery rate from 0.30 to 0.45 improves the economic objective by approximately 7.4%, while simultaneously enhancing environmental performance. These findings demonstrate the value of integrated decision-making and provide actionable guidance for supply chain managers regarding channel-management strategies, food-waste recovery investments, and sustainable network design.