BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:Chathuri Aththanayake Mukaweti Sahabandu Mudiyanselage to pres
 ent Doctoral Research
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260616T053103Z
UID:tag:localist.com\,2008:EventInstance_49487921812412
DTSTART:20250509T150000Z
DTEND:20250509T160000Z
DESCRIPTION:The UTC Graduate School is pleased to announce that Chathuri At
 hthanayake Mukaweti Sahabandu Mudiyanselage will present Doctoral research
  titled\, Uncertainty on Multi-objective Discrete Optimization on 05/09/20
 25 at 11.00 AM - 1.00PM in Room 393 (LUPH 393). Everyone is invited to att
 end. \n\nComputational Science\n\nChair: Dr. Lakmali Weerasena\n\nCo-Chair
 : \n\nAbstract:\n\nThis dissertation investigates decision-making in Uncer
 tain Discrete Multi-objective Optimization Problems (UDMOPs)\, where uncer
 tainty affects both the objective function and constraint coefficients. Cu
 rrent scientific findings indicate that solutions obtained from determinis
 tic approaches\, though efficient\, may not be practical under uncertainty
 . Additional research is therefore necessary to address these challenges. 
 The study pursues three primary research goals: (1) Constructing sensitivi
 ty regions in the objective space to address uncertainty in objective func
 tion values\; (2) Constructing sensitivity regions in the decision space t
 o manage feasibility uncertainties\, and (3): Developing methods to sort\,
  group\, and prune uncertain solutions based on the Decision-Maker’s (DM
 ’s) preferences. To achieve these goals\, we propose a set of methods th
 at explore and quantify uncertainty\, enabling the identification of both 
 low- and high-risk solutions aligned with the DM’s risk tolerance and pr
 eferences. The proposed approaches integrate optimization techniques to su
 pport risk-averse decision-making. Numerical experiments\, including real-
 world applications and benchmark comparisons\, demonstrate that under unce
 rtainty\, low- or high-risk solutions can outperform the efficient solutio
 ns derived from deterministic models. Overall\, this study provides a more
  consistent and informative decision-support framework for decision-makers
  facing uncertainty.
LOCATION:Lupton Hall\, 393
SUMMARY:Chathuri Aththanayake Mukaweti Sahabandu Mudiyanselage to present D
 octoral Research
URL;VALUE=URI:https://calendar.utc.edu/event/chathuri-aththanayake-mukaweti
 -sahabandu-mudiyanselage-to-present-doctoral-research
CATEGORIES:Lectures & Presentations
END:VEVENT
END:VCALENDAR