ML-Based Demand Forecasting in Fuzzy Inventory Models with Emission Costs: A Memory Effect Analysis - Keswani, Mamta (Journal of Environmental Accounting and Management, 2026)
This study develops a fuzzy logic-based inventory model for imperfect and deteriorating items, incorporating carbon emission costs and allowing for partially backlogged shortages. To reflect real-world uncertainties in parameters such as ordering cost, deterioration rate, and demand, triangular fuzzy numbers are employed. Fractional calculus is used to capture short- and long-term memory effects in the system. The model aims to minimize the total average cost, including emissions, by optimizing order quantity and replenishment cycles. Defuzzification is performed using the Centroid Method (CM) and Signed Distance Method (SDM). Machine Learning algorithms are applied for seasonal demand forecasting. Numerical results validate the model's effectiveness under memory-based behavior.