SMART EOQ MODELS: INCORPORATING AI AND MACHINE LEARNING FOR INVENTORY OPTIMIZATION - Rajnikant, Patel Nirmal; Khanna, Ritu (International Journal of Engineering Science Technologies, 2025)
Traditional Economic Order Quantity (EOQ) models rely on static assumptions (e.g., constant demand ????????, fixed holding cost ℎ), failing in volatile environments. This research advances dynamic inventory control through an AI-driven framework where: 1) Demand Forecasting: Machine learning (LSTM/GBRT) estimates time-varying demand: ????????ₜ = ???????? (????????ₜ; ????????) + ????????ₜ (????????ₜ: covariates like promotions, seasonality; ????????ₜ: residuals) 2) Adaptive EOQ Optimization Reinforcement Learning (RL) dynamically solves the following optimization problem: min????????????????,???????????????? ???????????????? ???????? (ℎ⋅????????????????++????????⋅????????????????−+????????⋅????????(????????????????))???? Subject to: ????????????????=????????????????−1+????????????????−???????????????? Where: • Q_t: Order quantity at time t • s_t: Reorder point at time t • h: Holding cost per unit • b: Backorder (shortage) cost per unit • k: Fixed ordering cost • δ(Q_t): Indicator function (1 if Q_t>0, else 0) • I_t^+: Inventory on hand (positive part of I_t) • I_t^-: Backordered inventory (negative part of I_t) • D_t: Demand at time t Validation was performed using sector-specific case studies. • Pharma: Perishability constraint ????????ₜ⁺ ≤ ???????? (????????: shelf-life) reduced waste by 27.3% • Retail: Promotion-driven demand volatility (????????²(????????ₜ) ↑ 58%) mitigated, cutting stockouts by 34.8% Automotive: RL optimized multi-echelon coordination, reducing shortage costs by 31.5%. The framework reduced total costs by 24.9% versus stochastic EOQ benchmarks. Key innovation: closed-loop control where ????????ₜ = RL(????????????????????????????????????????ₜ) adapts to real-time supply-chain states.