This paper proposes a centralized 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 centralized controller then collects the supply chain state information and optimizes 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.

Transport networks are large-scale complex systems whose objective is to deliver cargo at a specific time and at a specific location. Ports and intermodal container terminals behave as exchange hubs where containers are moved from a transport modality to a different one. Terminal operations management arise as a need to face the exponentially growth of the container traffic in the last few years. In this paper the Extended Formulation of the MPC is presented. This formulation accounts for the variation of the control action to reduce not only the amount of actions but to perform a wise and efficient use of handling resources. This formulation is based on the decomposition of the control action. The Extended Formulation is applied to a simulation case study based on a long-term scheduled scenario and compared with the Basic Formulation.

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