Stochastic Model Predictive Control of Supply Chains of Perishable Goods
F. P. Bernardini (Università di Pavia)
Jose Maria Maestre (University of Seville)
Pablo Velarde (Loyola University Andalusia)
R.R. Negenborn (TU Delft - Transport Engineering and Logistics)
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Abstract
This work presents a stochastic model predictive control approach to optimize the management of a meat supply chain with uncertain demand. The proposed approach considers the temperature-dependent deterioration of meat products and the multi-stage nature of the supply chain, including producers, warehouses, retailers, and customers. The management problem is formulated as a mixed-integer optimization problem, where the objective is to minimize the total cost of the supply chain while satisflying customer demand and quality requirements. The approach uses scenario-based optimization to account for different uncertainty sources. The results show that the proposed method effectively balances the conflicting objectives of minimizing costs and meeting demand and quality requirements while accounting for uncertainty.
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