Bernardini, F.P. , Maestre, J.M. , VELARDE RUEDA, PABLO ANIBAL, Negenborn, R.R.
No
Conference Paper
Científica
01/01/2025
2-s2.0-105018796892
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. © © 2025 The Authors.
Copyrights; Deterioration; Integer programming; Optimal control systems; Predictive control systems; Sales; Stochastic control systems; Stochastic models; Stochastic systems; Supply chain management; Meat products; Model-predictive control; Model-predictive control approach; Optimal controls; Perishable goods; Quality requirements; Stochastic control; Stochastic model predictive controls; Temperature dependent; Uncertain demand; Model predictive control; Supply chains