A new data-driven framework to select the optimal replenishment strategy in complex supply chains

• Published in: IFAC-PapersOnLine Vol. 55; no. 10; pp. 1423 - 1428
• Main Authors: Corsini, Roberto R., Costa, Antonio, Fichera, Sergio, Framinan, Josè M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2022
• Subjects: cyber-physical system; disruption; machine learning; metaheuristic algorithm; optimization; perishable product; simulation; Supply chain dynamics; Supply chain dynamics; disruption; optimization; simulation; perishable product; cyber-physical system; machine learning; metaheuristic algorithm

Motivated by the high variability of markets occurred in the last years, which in turns determined significant uncertainty in lead times and supply chain dynamics, this paper introduces a data-driven framework based on machine learning and metaheuristic optimization to dynamically select the most su...