Stochastic Programming and
Statistically Assessing Solution Quality
David Morton
Professor
Industrial Engineering and Management Sciences
Northwestern University
Thursday, March 2, 2017
4:15 p.m.
Marrs McLean Science Building 301
Abstract
Engineering systems requiring design or control often have uncertainties arising from an inability to provide precise point estimates of key model parameters. Stochastic programming
provides an appropriate modeling framework for many such systems by assuming a probability distribution, or stochastic process, governs the uncertain parameters. We discuss two
such applications in rapidly detecting cellphone viruses, and in stockpiling ventilators for
an influenza pandemic. Simply evaluating the objective function of a stochastic program
can be intractable because of a high-dimensional expectation, necessitating approximations.
Using a sample mean obtained from Monte Carlo simulation yields a sample average approximation (SAA) to the original stochastic program. We explore how SAA can be used
to assess the quality of a candidate solution.
Dave Morton is a Professor of Industrial Engineering and Management Sciences
at Northwestern University. He received a B.S. in Mathematics and Physics from
Stetson University and an M.S. and Ph.D. in Operations Research from Stanford
University. Prior to joining Northwestern, he was on the faculty at the University
of Texas at Austin, worked as a Fulbright Research Scholar at Charles University in
Prague, and was a National Research Council Postdoctoral Fellow in the Operations
Research Department at the Naval Postgraduate School.
Please join us for refreshments in MMSCI 179 at 3:45.