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

  title={An application of chance-constrained model predictive control to inventory management in Hospitalary Pharmacy},
  author={J. Torreblanca and P. Velarde and I. Jurado and C. Ocampo-Martinez and I. Fernandez and B. I. Tejera and J. R. P. Llergo},
  journal={53rd IEEE Conference on Decision and Control},
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… Expand
An application of economic model predictive control to inventory management in hospitals
Experimental results from the application of model predictive control to inventory management in a real hospital show that the adopted approach outperforms the method employed by the hospital and reduces both the average stock levels and the work burden of the pharmacy department. Expand
Stock management in hospital pharmacy using chance-constrained model predictive control
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. Expand
A Data-Based Model Predictive Decision Support System for Inventory Management in Hospitals
Experimental results from the application of a data-based model predictive decision support system to drug inventory management in the pharmacy of a mid-size hospital in Spain are presented to improve the efficiency of their inventory policy by exploiting pharmacy historical data. Expand
Stochastic model predictive control approaches applied to drinking water networks
Summary Control of drinking water networks is an arduous task, given their size and the presence of uncertainty in water demand. It is necessary to impose different constraints for ensuring aExpand
Stochastic model predictive control for robust operation of distribution systems
In this work, an analysis and comparison regarding performance among the three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained, are carried out and the possibility of application in several distribution sectors is analyzed. Expand
Stochastic linear Model Predictive Control with chance constraints – A review
The main ideas underlying SMPC are presented and different classifications of the available methods are proposed in terms of the dynamic characteristics of the system under control, the performance index to be minimized, the meaning and management of the probabilistic constraints adopted, and their feasibility and convergence properties. Expand
QORAl: Q Learning based Delivery Optimization for Pharmacies
A Q-Learning based approach for optimising routes with minimum computation time, in the form of the QORAl algorithm is provided for home delivery in a multi-source, multi-destination scenario. Expand


Model Reference Control in Inventory and Supply Chain Management - The Implementation of a More Suitable Cost Function
A method of model reference control is investigated in this study in order to present a more suitable method of controlling an inventory or a supply chain. The problem of difficult determining of theExpand
Application of robustified Model Predictive Control to a production-inventory system
A trade-off between robust stability towards parametric uncertainties and nominal performances for the nominal system is highlighted, and a convex optimization problem is solved with LMI tools. Expand
A model predictive control strategy for supply chain management in semiconductor manufacturing under uncertainty
Model predictive control (MPC) is presented as a tactical decision module for supply chain management in semiconductor manufacturing. A representative problem which includes distinguishing featuresExpand
A novel model predictive control algorithm for supply chain management in semiconductor manufacturing
Supply chains in semiconductor manufacturing are characterized by integrating dynamics, nonlinearity and high levels of stochasticity. In this paper, we present a novel model predictive control (MPC)Expand
Chance-Constrained Model Predictive Control for Drinking Water Networks
This paper addresses a chance-constrained model predictive control (CC-MPC) strategy for the management of drinking water networks (DWNs) based on a finite horizon stochastic optimisation problemExpand
Advances and applications of chance-constrained approaches to systems optimisation under uncertainty
A brief survey of major application areas, structure properties, challenges and solution approaches to CCOPT is presented and the research results achieved in the past few years are presented. Expand
Model predictive control in the process industry
Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing theExpand
Chance‐constrained model predictive control
This work focuses on robustness of model-predictive control with respect to satisfaction of process output constraints. A method of improving such robustness is presented. The method relies onExpand
Quantitative Models for Supply Chain Management
From the Publisher: Quantitative models and computer based tools are essential for making decisions in today's business environment. These tools are of particular importance in the rapidly growingExpand
Stochastic Linear Programming
This book is a definitive presentation and discussion of the theoretical properties of the models, the conceptual algorithmic approaches, and the computational issues relating to the implementation of these methods to solve problems that are stochastic in nature. Expand