Emagister Eventos

Optimización con Incertidumbre

  • Fechas:

    Del 11/04/19 al 11/04/19

  • Lugar:

    Sala de teledocencia, Escuela de Doctorado. Casa del Estudiante, calle Real de Burgos,Valladolid., Valladolid, España (mapa)

Web del evento

Descripción ↑ subir

Curso sobre Optimización con Incertidumbre

 

Impartido por el profesor Ignacio E. Grossmann

Carnegie Mellon University, EEUU

Día 11 de abril de 2019

de 10:00 a 14:00 y de 16:00 a 19:00,

Aula Teledocencia, Escuela de Doctorado
Casa del Estudiante, C/ Real de Burgos s/n  47011 Valladolid

El curso es presencial pero, además, se retransmitirá por videoconferencia para quienes deseen asistir de manera remota.

Inscripción

La inscripción es gratuita. Para realizar la misma (tanto presencial como remota) se debe acceder al formulario a través del enlace situado en la parte superior derecha de esta mísma página.

El número de plazas está  limitado a 30 asistentes en forma presencial y 24 en forma remota. Debido a estos límites, en el aforo virtual es posible que se permita sólo una conexión por localización, para que se realice la visualización del curso por grupos de usuarios, en lugar de tener una conexión a un ordenador personal por asistente.

Una vez rellenado el formulario, los solicitante recibirán un correo electrónico confirmando la inscripción. Si la inscripción ha sido realizada para la asistencia de manera remota, pocos días antes del curso recibirá un nuevo correo electrónico con el enlace web adecuado, así como una hoja con instrucciones para poder seguir y aprovechar de la mejor manera el curso a distancia.

Evento organizado por el Instituto de Procesos Sostenibles / GIR de Control y Supervisión de Procesos de la UVA

Lugar ↑ subir

Programa ↑ subir

Optimization under uncertainty has been an active and challenging area of research for many years. However, its application in Process Synthesis has faced a number of important barriers that have prevented its effective application. Barriers include availability of information on the uncertainty of the data (ad-hoc or historical), determination of the nature of the uncertainties (exogenous vs. endogenous), selection of an appropriate strategy for hedging against uncertainty (robust optimization vs. stochastic programming), handling of nonlinearities (most work addresses linear problems), large computational expense (orders of magnitude larger than deterministic models), and difficulty in the interpretation of the results by non-expert users.

 

In this lecture, we describe recent advances that address some of these barriers. We first describe the basic concepts of robust optimization, including the robust counterpart, showing its connections with semi-infinite programming. We also we explore the relationship between flexibility analysis and robust optimization for linear systems. A historical perspective is given, which shows that some of the fundamental concepts in robust optimization have already been developed in the area of flexibility analysis in the 1980s. We next consider two-stage and multi-stage stochastic programming in the case of exogenous parameter, for which we describe acceleration techniques for Benders decomposition, hybrid sub-gradient/cutting plane methods for Lagrangean decomposition, and sampling techniques. We then address the generalization to the case of both exogenous and endogenous parameters, which gives rise to conditional scenario trees for which theoretical properties are described to reduce the problem size. To avoid ad-hoc approaches for setting up the data for these problems, we describe approaches for handling of historical data for generating scenario trees. We illustrate the application of each of these formulations in demand-side management optimization, planning of process networks, chemical supply chains under disruptions, planning of oil and gas fields, and optimization of process networks, all of them under some type of uncertainty. Finally, we briefly discuss recent research in stochastic programming dealing with one and two-stage discrete two-stage decision and nonlinear problems.

Ponentes ↑ subir

Prof. Ignacio E. Grossmann is the Rudolph R. and Florence Dean University Professor of Chemical Engineering, and former Department Head at Carnegie Mellon University.  He obtained his B.S. degree in Chemical Engineering at the Universidad Iberoamericana, Mexico City, in 1974, and his M.S. and Ph.D. in Chemical Engineering at Imperial College in 1975 and 1977, respectively. After working as an R&D engineer at the Instituto Mexicano del Petróleo in 1978, he joined Carnegie Mellon in 1979. He was Director of the Synthesis Laboratory from the Engineering Design Research Center in 1988-93. He is director of the "Center for Advanced Process Decision-making" which comprises a total of 20 petroleum, chemical and engineering companies. Ignacio Grossmann is a member of the National Academy of Engineering , Mexican Academy of Engineering, and associate editor of AIChE Journal and member of editorial board of Computers and Chemical Engineering, Journal of Global Optimization, Optimization and Engineering, Latin American Applied Research, and Process Systems Engineering Series. He was Chair of the Computers and Systems Technology Division of AIChE, and co-chair of the 1989 Foundations of Computer-Aided Process Design Conference and 2003 Foundations of Computer-Aided Process Operations Conference. He is a member of the American Institute of Chemical Engineers, Institute for Operations Research and Management Science, Mathematical Optimization Society, and American Chemical Society.

Eventos relacionados