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Title:Effective continuous-flow supply chains using centralized model predictive control
Authors:T. Hipolito, J.L. Nabais, M.A. Botto, R.R. Negenborn

Conference:21st IFAC World Congress (IFAC2020)
Address:Berlin, Germany
Date:July 2020

Abstract:This paper proposes three different formulations of centralized model predictive control to manage a continuous-ow supply chain subject to fluctuating demand. The supply chain is modeled as a dynamic system composed of multiple agents moving commodities from manufacturer to retailer. Commodities are differentiated according their nature (raw material or perishable good) and type (1 or 2). Furthermore, a virtual agent gathers information from all agents regarding the amount of commodities stored, the maximum storage capacity, the available moving capacity and the expected demand regarding the present and future instants. Taking into account customer demand must be satisfied by available inventory, the controller coordinates the retailer's inventory, minimizing commodity movements and storage. Consequently, three different formulations of the model predictive control algorithm are designed based on retailer's inventory: i) fixed retailer's inventory, ii) dynamic heuristic retailer's inventory, and iii) dynamic control retailer's inventory. These formulations are simulated for a "just-in-time" management policy, obtained from the manipulation of the weights of the cost function of the optimization problem. The performance of the distinct formulations is evaluated based on the amount of commodity movements.

Reference:T. Hipolito, J.L. Nabais, M.A. Botto, R.R. Negenborn. Effective continuous-flow supply chains using centralized model predictive control. Accepted for the 21st IFAC World Congress (IFAC2020), Berlin, Germany, July 2020.
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