BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Date iCal//NONSGML kigkonsult.se iCalcreator 2.20.2//
METHOD:PUBLISH
X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:STANDARD
DTSTART:20181028T030000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20190331T020000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:calendar.18141.field_data.0@www.open.diag.uniroma1.it
DTSTAMP:20260406T200255Z
CREATED:20190310T212153Z
DESCRIPTION:Prof. Aharan Ben-Tal\, Professor of Operations Research at the 
 Faculty of Industrial Engineering and Management at the Technion – Israel 
 Institute of Technology\, will give the following two seminars for the ABR
 O PhD course:March 13 (10:00-12:00) Room A4 Robust optimization: the need\
 , the challenge\, the success.March 14 (10:00-12:00) Room Aula Magna Multi
 -stage (dynamic) optimization problems affected by uncertaintyABSTRACTLECT
 URE 1We list and illustrate by examples various sources of uncertainty ass
 ociated with optimization problems. We then explain the difficulties arisi
 ng when solving such uncertainty affected problems due to lack of full inf
 ormation on the nature of the uncertainty on one hand\, and the likelihood
  of facing computationally intractable problems on the other hand. Robust 
 Optimization (RO) is a methodology that was designed from the start to mee
 t the above challenges. We will review the theory underlying the RO method
 ology and demonstrate its success in solving meaningful static conic probl
 ems (linear\, conic quadratic and semidefinite programs) affected by uncer
 tainty\, and demonstrate their success in solving some challenging Enginee
 ring problems. LECTURE 2We introduce RO methodology\, based on (mainly) li
 near decision rules\, to treat multi- stage optimization problems affected
  by uncertainty. The methodology is used to solve a supply chain problem.W
 e then address problems\, including Chance Constrained ones\, where stocha
 stic parameters suffer from distributional ambiguity\, and show how RO can
  provide tractable approximations for these hard problems. 
DTSTART;TZID=Europe/Paris:20190313T100000
DTEND;TZID=Europe/Paris:20190314T100000
LAST-MODIFIED:20200521T211813Z
LOCATION:DIAG - Via Ariosto 25
SUMMARY:Robust optimization - Aharon Ben-Tal
URL;TYPE=URI:http://www.open.diag.uniroma1.it/node/18141
END:VEVENT
END:VCALENDAR