Effective continuous-flow supply chains using centralized model predictive control
Tomas Hipolito (Universidade Técnica de Lisboa)
Joao Lemos Nabais (Escola Superior Saúde-Instituto Politécnico de Setúbal, Universidade Técnica de Lisboa)
Miguel Ayala Botto (Universidade Técnica de Lisboa)
R. Negenborn (TU Delft - Transport Engineering and Logistics)
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Abstract
This paper proposes three different formulations of a centralized Model Predictive Control framework to manage the logistics of continuous-flow Supply Chains subject to fluctuating demand. The Supply Chain is modeled as a dynamical system composed of several players handling commodities from the production phase to the retail phase. Additionally, commodities are categorized according to their characteristics. An external control agent continuously gathers information regarding Supply Chain operation. Using that information, the control agent monitors the inventory of the retailer and assigns the commodity quantity to replenish it, adopting a Model Predictive Control algorithm. Three different formulations of the Model Predictive Control algorithm are designed based on the inventory of the retailer: i) constant inventory, ii) dynamical heuristic inventory, and iii) dynamical control inventory. These formulations are simulated for a Supply Chain operating under a "just-in-time"management policy.
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