Pablo Velarde

Stock management in hospital pharmacy using chance-constrained model predictive control

One of the most important problems in the pharmacy department of a hospital is stock management. The clinical need for drugs must be satisfied with limited work labor while minimizing the use of economic resources. The complexity of the problem resides in the random nature of the drug demand and the multiple constraints that must be taken into account in every decision. In this article, chance-constrained model predictive control is proposed to deal with this problem. The flexibility of model predictive control allows taking into account explicitly the different objectives and constraints involved in the problem while the use of chance constraints provides a trade-off between conservativeness and efficiency. The solution proposed is assessed to study its implementation in two Spanish hospitals.

Application of robust model predictive control to inventory management in hospitalary pharmacy

Inventory management is one of the main tasks that the pharmacy department has to carry out in a hospital. It is a complex problem that requires to establish a tradeoff between different and contradictory optimization criteria. The complexity of the problem is increased due to the constraints that naturally arise in this type of applications. In this paper, which corresponds to preliminary works performed to implement robust and advanced control techniques for pharmacy management in two Spanish hospitals, we propose, assess and compare three robust model predictive control(Chance-Constraints, Multi-Scenarios approach and Tree-Based Methods) as a mean to relieve this issue.

An application of chance-constrained model predictive control to inventory management in Hospitalary Pharmacy

Inventory management is one of the main tasks that the pharmacy department has to carry out in a hospital. It is a complex problem that requires to establish a tradeoff between different and contradictory optimization criteria. The complexity of the problem is increased due to the constraints that naturally arise in this type of applications. In this paper, which corresponds to preliminary works performed to implement advanced control techniques for pharmacy management in two Spanish hospitals, we propose and assess chance-constrained model predictive control (CC-MPC) as a mean to relieve this issue.

Vulnerabilities in Lagrange-Based DMPC in the Context of Cyber-Security

Autonomic computing requires reliable coordination between different systems. The unexpected behavior of any component may endanger the performance of the overall system. For this reason, it is necessary to prevent and detect this type of situations and to develop methods to react accordingly and to mitigate the possible consequences. In this work, we present an analysis of the vulnerability of a distributed model predictive control (DMPC) scheme in the context of cybersecurity. We consider different types of so-called insider attacks. In particular, we consider the presence of a malicious controller that broadcasts false information to manipulate costs for its own benefit. Also, we propose a mechanism to protect or, at least, relieve the consequences of the attack in a typical DMPC negotiation procedure. More specifically, a consensus approach that dismisses the extreme control actions is presented as a way to protect the distributed system from potential threats. Simulations are carried out to illustrate both the consequences of the attacks and the defense mechanisms.

Application of robust model predictive control to a renewable hydrogen-based microgrid

In order to cope with uncertainties present in the renewable energy generation, as well as in the demand consumer, we propose in this paper the formulation and comparison of three robust model predictive control techniques, i.e., multi-scenario, tree-based, and chance-constrained model predictive control, which are applied to a nonlinear plant-replacement model that corresponds to a real laboratory-scale plant located in the facilities of the University of Seville. Results show the effectiveness of these three techniques considering the stochastic nature, proper of these systems.