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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With the advent of on-board Direct Current (DC) power and propulsion systems, the transmission and delivery of energy on board of ships can be carried out more efficiently as it is being done using conventional direct-diesel or Alternative Current (AC) power and propulsion systems. However, the stability of DC voltage on-board of all-electric ships with a DC power and propulsion architecture is a critical issue that has drawn attention over the last few years. In this paper, a novel Model Predictive Control (MPC) approach is proposed for the diesel-generator shaft speed control and DC voltage regulation on-board of all-electric ships, focusing on the uncontrolled rectification at the voltage conversion stage. This work considers the prime mover as a Diesel–Generator–Rectifier (DGR) set which feeds propulsive asynchronous motors through a DC-link. First, a state space model dynamic model is developed for the DGR set and the DC-link. Then, the MPC-based approach is presented. The approach is based on Input–Output Feedback Linearization (IOFL) which is used for the linearization of the highly non-linear dynamics of the system. To increase the robustness of the algorithm, a tube-based technique is adopted which is implemented through a linear auxiliary control law. Different analyses are carried out to show that the proposed control strategy is capable of handling sudden changes in load conditions as well as adverse effects of Constant Power Loads (CPL).@en

In this paper, a control strategy is proposed for the voltage regulation and the shaft speed control of diesel generators on-board of all-electric ships with Direct Current (DC) power and propulsion systems. The proposed methodology is based on Input-Output Feedback Linearization (IOFL) of the prime mover dynamical model. First, a model for different components in the system is represented and by merging them, the overall model of the system is obtained in state space format. Then, an IOFL-based control algorithm is applied for stabilization, voltage regulation and shaft speed control of the diesel generator. The performance of the algorithm is assessed using a model of an inland vessel.@en

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