Location: Room SF3103, 3rd floor, Sandford Fleming Building, 10 King’s College Road, University of Toronto.   MAP

In this talk, we will give an introduction to modeling optimization problems where some parameters of the problem are uncertain and modeled as random variables. For instance, in power generation, energy demand is highly uncertain as well as renewal power supply at the time of the generation; in a transportation problem, travel times are uncertain at the time of planning/scheduling. We will introduce two-stage stochastic programming, and statistics that measure the value of computing a solution to the stochastic problem. We will show how to create an equivalent extensive form (deterministic) formulations of the instances, so that they may be solved with standard optimization software. We will illustrate the presented ideas on our ongoing work on the stochastic mobile facility location problem in crowdshipping.

Merve Bodur is an Assistant Professor in the Department of Mechanical and Industrial Engineering at the University of Toronto. She also holds a Dean’s Spark Professorship in the Faculty of Applied Science and Engineering. She is a faculty associate of University of Toronto Transportation Research Institute, Smart Freight Centre, and Centre for Healthcare Engineering at the University of Toronto. She obtained her Ph.D. from University of Wisconsin-Madison and did a postdoc at Georgia Institute of Technology. She received her B.S. in Industrial Engineering and B.A. in Mathematics from Bogazici University, Turkey. Her research interests include stochastic programming, integer programming, multiobjective integer programming and combinatorial optimization, with applications in a variety of areas such as scheduling, transportation, power systems, healthcare and telecommunication.

This seminar is presented by the University of Toronto Institute of Transportation Engineers (ITE) Student Chapter.