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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This paper proposes a centralised model predictive control framework to address logistics management of supply chains of perishable goods. Meeting customer specific requirements is decisive to gain a competitive advantage in supply chain management. This fact motivates stakeholders to address solutions that continuously improve supply chain operations. The solution proposed in this work considers the supply chain as a dynamical system in a state-space representation where different categories of commodities, namely common goods and perishable goods, are included. Additionally, the dynamical model is able to store information of the complete supply chain regarding the quantity of commodities and the due time associated to the perishable goods. A centralised controller then collects the supply chain state information and optimises the commodity flow based on the model prediction over a fixed time horizon. The model predictive control solution assigns just-in-time commodity flows, schedules production according to customer demand (pull system) and monitors work-in-progress and in-transit commodities. The success of the proposed control approach is demonstrated in a numerical simulation of a three-tier supply chain following three distinct management policies.

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The movement of perishable goods is growing worldwide. Perishable goods need to be available to the market before the expiration date. With the decrease in inventory levels the components of a supply chain become even more integrated and dependent on coordinated decisions. Information regarding perishable goods must be visible throughout the supply chain for avoiding goods loss. A Model Predictive Control (MPC) heuristic to address operations management at supply chains of perishable goods is proposed in this paper. The approach is capable to follow the remaining time until expiration date which is critical to avoid losses. The supply chain is modeled using a state-space representation and operations management at the supply chain is formulated as anMPC Problem. In order to cope with operational decisions, the problem is solved on a periodic basis. The proposed approach is capable to deal with production decisions, monitor work-in-progress (WIP), and make transport assignments while monitoring the remaining time until the expiration date. Flows over the supply chain can be synchronized and therefore we named this type of supply chain a Synchronized Supply Chain (SSC). The approach is modular and easily scalable for largescale supply chains. Numerical results illustrate these statements.@en