A Robust Sustainable Inventory Model for Deteriorating Products with Carbon-Emission Costs, Learning-Dependent Demand, and Partial Backlogging under Hybrid Uncertainty

Abstract

Conditions in the environment, operational costs, as well as product wastage make it essential to have sustainable methods of managing inventories for perishable commodities. The paper suggests an efficient inventory management methodology. Carbon emissions' cost considerations, learning about demands based on demands' dependence on learning, as well as partial backlog based on market variability related to consumer behavior, form part of variability related to ‘hybrid uncertainty.’ The idea assumes progression in demands as consumers learn about their product (learning effect), considering their instability related to market conditions. A particular product degrades over time. The backlog level related to shortages is partially fulfilled, depending on consumer behavior related to wait time. To minimize the cost associated with inventory, appropriate optimization strategies are employed. These costs would encompass purchase costs, storage costs, shortage costs, deterioration costs, and carbon emissions. Decisions about inventories depend on learning behavior, uncertainties, and sustainability laws, as results from sensitivity analyses and numerical experiments have shown. The results make it clear that learning and the costs of emissions must be considered to come up with viable and sustainable solutions. Decision-makers can learn from these results to find a way to combine environmental and financial success. The presented method helps to optimize financial and sustainability goals simultaneously.