Effective Continuous-flow Supply Chains Using Centralized Model Predictive Control

• Published in: IFAC-PapersOnLine Vol. 53; no. 2; pp. 10855 - 10860
• Main Authors: Hipólito, Tomás, Nabais, João Lemos, Botto, Miguel Ayala, Negenborn, Rudy R.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2020
• Subjects: Commodity Categorization; Continuous-flow Supply Chains; Logistics in Manufacturing; Model Predictive Control; Modeling of Manufacturing Operations; Continuous-flow Supply Chains; Commodity Categorization; Logistics in Manufacturing; Modeling of Manufacturing Operations; Model Predictive Control
• ISSN: 2405-8963; 2405-8971; 2405-8963
• DOI: 10.1016/j.ifacol.2020.12.2808

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.