A stochastic predictive control approach to project risk management

This work shows a control policy based on MPC and applied to project risk management. MPC has been applied due the properties that presents such as the easy constraint treatment or the extension to multivariable case. The control actions are the mitigation actions to execute in order to reduce the risk exposure. Stochastic variables have been introduced to model the uncertainties of risk impacts. Integer variables are involved in the optimization problem modelling the mitigation actions

Hybrid algorithm for scheduling and risk assessment of projects

IFAC CONFERENCE ON ANALYSIS AND DESIGN OF HYBRID SYSTEMS (.2003.SAINT-MALO BRITTANY, FRANCIA)This work presents a technique for optimal scheduling of projects in terms of time and cost, taking into account risk assessment. Tasks are characterized by p-timed Petri nets, where places have assigned an execution time. The proposed technique minimizes the time execution and the cost of the whole project taking into account the Petri nets describing the tasks and the project risk assessment plan. The risk mitigation is carried on through actions where variables that model them may be discrete or continuousMinisterio de Ciencia y Tecnología DPI200 1-2380-C02-0

Un sistema de decisión multicriterio basado en riesgos: aplicación a la fase de ofertas

XXIV JORNADAS DE AUTOMÁTICA (24) (24.2003.LEÓN, ESPAÑA)Este trabajo presenta un sistema de soporte de decisión para proporcionar ayuda en la fase de ofertas, caracterizada por un alto nivel de incertidumbres. La preparación de la propuesta involucra un coste considerable, sumado a una gran movilización de recursos. En la práctica, usualmente las ofertas son evaluadas en base a diferentes criterios o parámetros de decisión. El algoritmo propuesto evalúa los distintos candidatos a propuesta según las distintas configuraciones de criterios. Se ha introducido una estructura basada en riesgos para minimizar una función objetivo que contiene las posibles acciones mitigadoras que pueden eliminar, parcial o totalmente, los daños causados por riesgos. Las acciones mitigadoras pueden tener una naturaleza discreta o continua

Fault quantifcation and mitigation method for energy management in microgrids using MPC reconfiguration

The current energy situation and the possibility of exhausting fossil fuels in a relatively near period, have led to investing efforts in the development of techniques that use renewable energy sources for power generation. A configuration that allows renewable energy sources to be integrated into the overall power system, advocates dividing the grid into distributed systems incorporating small-scale generation and storage. Microgrids are a well-known type of these systems. Control systems help maintain the reliability of the energy supply while minimizing costs. In addition, it must be taken into account that faults can occur in the processes that make up the microgrid. In some cases, the control system can mask these faults, even allowing the fault to reach an irreparable level. In this context, fault-tolerant control is a tool that enables control objectives to be maintained even in the presence of faults. If necessary, the control objectives are adapted to the fault. Furthermore, the fault tolerant control system needs to be able to detect faults, quantify their intensity and act accordingly. In this way it is avoided that small faults, that in other circumstances would remain hidden by the control loop, cause faults of a greater magnitude. This article proposes a fault quantification method based on parity equations and structured residuals that, together with a fault accommodation tolerance mechanism, mitigates the consequences of possible faults in this type of system

A Risk-Based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

This brief examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based model predictive control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this brief can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm

Model predictive control for microgrid functionalities: review and future challenges

ABSTRACT: Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.info:eu-repo/semantics/publishedVersio

Evaluación multicriterio para la optimización de redes de energía

Este trabajo propone una herramienta para ayudar en la toma de decisión de la planificación de redes de energía. Se incluye una evaluación multicriterio de los escenarios posibles de planificación considerando distintos criterios y ponderaciones. Para este cometido se usa la herramienta multiobjetivo discreta PROMETHEE y planos GAIA. El conjunto de escenarios se genera considerando incertidumbres que puede presentar el sistema. La simulación de la red eléctrica se lleva a cabo en la herramienta comercial OpenDSS. Para ilustrar la herramienta se ha tomado una red de la IEEE, donde se observan los beneficios del método propuesto. Los resultados obtenidos muestran que el tomar incertidumbres en el proceso de optimización de las redes de potencia, supone un gran aumento en la eficiencia de la red.Ministerio de Economía, Industria y Competitividad de España Proyecto CONFIGURA DPI2016-78338-

Using a risk-based approach to project scheduling: A case illustration from semiconductor manufacturing

This paper introduces a risk-based optimization method to schedule projects. The method uses risk mitigation and optimal control techniques to minimize variables such as the project duration or the cost estimate at completion. Mitigation actions reduce the risk impacts that may affect the system. A model predictive control approach is used to determine the set of mitigation actions to be executed and the time in which they are taken. A real-life project in the field of semiconductor manufacturing has been taken as an example to show the benefits of the method in a deterministic case and a Monte Carlo simulation has also been carried out.

A decision support system for bidding process

2002 IFAC15th Triennial World Congress, Barcelona, SpainThis article has been developed in the frame of an IST European Project where Companies and Universities of several countries of Europe have collaborated. The work presents a Decision Support System (DSS) to provide help in the bidding process. Critical decisions as bid/no bid make/buy or decision of best final proposal have been realised. The tool performs a risk analysis and it uses the results in all the DSS phases

A multicriteria risk-based dss for bidding using mixed integer programming

FAC Control Applications of Optimisation, Visegnid, Hungary, 2003This work presents a Decision Support System to provide help in bidding processes. This phase of the project is characterised by a high level of uncertainty and it involves a huge expense in the preparation of the proposal and important mobilization of resources. In industrial practice, bids are usually evaluated on the basis of multiple criteria; this algorithm evaluates candidates according to different criteria configurations. A risk-based approach has been incorporated in the procedure in order to minimise an objective function that involves the mitigation actions of risks. Mitigation actions can own a discrete or continuous nature