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Oklahoma State University

IEM Seminar Series

The School of Industrial Engineering and Management in conjunction with the OSU INFORMS Student Chapter sponsors a Seminar Series in the fall and spring semesters. Various topics are covered by speakers from OSU and other organizations. The seminars are held on Wednesdays from 1-2:30 pm in the Fall semester and on Thursdays from 3:30-5:00 pm in the Spring semester. Seminar topics, location and a short abstract and bio about the presenters are posted below as it becomes available.

 

Date

Speaker

Title

Institution

Jan. 17th

Dr. Ashesh Kumar Sinha

Stochastic Models for Strategic Sourcing in Supply Chains

Schneider International

Jan. 19th

Mr. Hao Yan

Sequential High-Dimensional Data Analysis for Anomaly Detection and System Monitoring

Georgia Institute of Technology

Jan. 24th

Dr. Devashish Das

Data Analytics for Complex Systems: Improving the Science of Health Care Delivery

Mayo Clinic

Jan. 26th

Mr. Juan Borrero

Sequential Max-Min Bilevel Programming with Incomplete Information and Learning

University of Pittsburgh

Mar. 9th

Mr. G. Satish

SMAC Impact-Social, Mobile, Analytics, Cloud, and the Engineer

Connixt

Mar. 23rd

Dr. Suvrajeet Sen

Learning Enabled Optimization: Towards a Fusion of Stochastical Learning and Stochastic Optimization

University of Southern California

Mar. 24th

Dr. Julie Higle*

Modeling and Analysis for Cancer Screening

University of Southern California

Mar. 30th

Dr. Art Chaovalitwongse

Optimization in Medical Analytics: From Data to Knowledge to Decisions

University of Arkansas

Apr. 13th

Dr. James Tien

Internet of Things, Real-Time Decision Making, and Artificial Intelligence

University of Miami

Apr. 20th

Dr. David Morton

Optimizing Prioritized and Nesting Solutions

Northwestern University

*This seminar occurs on Friday at a time to be determined.

 

Industrial Engineering and Management Seminar Series
Sponsored by IEM and OSU INFORMS Student Chapter

Optimizing Prioritized and Nested Solutions 

 

Speaker: David Morton

Date: Thursday, April 20th 2017

Time: Seminar 3:30pm--4:30pm Q & A 4:30--5:00pm

Location: Engineering North 450

 

Abstract: A typical optimization model in operations research allocates limited resources among competing activities to derive an optimal portfolio of activities. In contrast, practitioners often form a rank-ordered list of activities, and select those with the highest priority, at least when choosing an activity is a yes-no decision. Ranking schemes that score activities individually are well known to be inferior. So, we describe a class of two-stage stochastic integer programs that accounts for structural and stochastic dependencies across activities and constructs an optimized priority list. We further discuss a class of optimization models, subject to a single "budget" constraint, that naturally leads to a family of optimal nested solutions at certain budget increments. We use several applications to both motivate the work and illustrate results, ranging from a stochastic facility location model to a hierarchical graph clustering problem. We also describe possible extensions. 

 

Speaker Bio: David Morton is a Professor of Industrial Engineering and Management Sciences at Northwestern University. His research interests include stochastic and large scale optimization with applications in security, public health, and energy systems. 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. He currently directs Northwestern’s center for Optimization and Statistical Learning.

http://osl.northwestern.edu