The United States generates over 80 million tons of food waste annually, with expired dairy among the highest-impact yet least-recovered retail categories. Expired milk is routinely disposed through municipal sewers, generating approximately 150 kg CO2 per tonne through biochemical oxygen demand treatment and fugitive methane, while destroying its biogas energy recovery potential through anaerobic digestion.

This paper presents an AI-driven multi-objective optimization framework routing expired milk to its highest-value end use, selecting dynamically among anaerobic digestion for biogas recovery, composting, and sewer disposal based on full lifecycle net CO2. The framework integrates a Haversine distance calculator, facility acceptance forecaster, constrained net CO2 optimizer, and greedy sequential assignment algorithm enforcing AD capacity constraints.

Validated through a 30-day simulation across five retail locations in Hammond-Chicago and three processing facilities, the framework routes 12,120 kg of expired milk across 150 daily records. Results demonstrate 45.4% reduction in net lifecycle CO2, recovery of 1,296 kWh biogas energy, 98% diversion from sewer, and zero AD overload events, validated using Lean Six Sigma DMAIC. A key finding: the AI route drives more kilometers yet produces 45% less lifecycle CO2, because sewer process penalties and AD energy credits dominate over transport distance.

This research contributes the first integrated AI framework linking retail perishable reverse logistics with AD biological constraints through a net lifecycle CO2 objective function, generalizable to meat, brewery, seafood, and pharmaceutical supply chains.

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Mr. Jayank Maanas Ranganadham