Monday, September 25

3108 Etcheverry Hall, 3:30 - 5:00 p.m.

Abstract: A product is said to be opaque if one or more of its attributes are not revealed until after the product has been sold. Opaque products have historically been used in the travel industry where airline and hotel brands might be hidden to the customer, in exchange for a discount. More recently, online retailers have also used opaque products, where customers can sacrifice their choice of color for a better price. The value of opaque products stems from their ability to i) price discriminate and ii) balance inventory. In this talk, we provide a set of tools for pricing and managing inventory with opaque products. We also explicitly characterize and quantify the price discrimination and inventory balancing effects. Finally, we describe how opaque products can be used as an alternative for discriminatory and dynamic pricing strategies. This is based on joint works with Michael Hamilton (Columbia), Yehua Wei (Boston College), and Yeqing Zhou (Columbia). 

Bio: Adam Elmachtoub is an Assistant Professor of Industrial Engineering and Operations Research at Columbia University, where he is also a member of the Data Science Institute. In 2014-2015, he spent one year at the IBM T.J. Watson Research Center working in the area of Smarter Commerce. His research currently focuses on designing new approaches for supply chain and revenue management, especially where the two areas collide. More broadly, he is also interested in leveraging data to make informed decisions in industries such as retail, logistics, and travel. He previously received his B.S. degree from Cornell in 2009, and my Ph.D. from MIT in 2014. In 2016, he received an IBM Faculty Award and was named in Forbes 30 under 30 in science.

 

Monday, October 2

3108 Etcheverry Hall, 3:30 p.m. - 5:00 p.m.

We have developed the ALAMO methodology with the aim of producing a tool capable of using data to learn algebraic models that are accurate and as simple as possible. ALAMO relies on (a) integer nonlinear optimization to build low-complexity models from input-output data, (b) derivative-free optimization to collect additional data points
that can be used to improve tentative models, and (c) global optimization to enforce physical constraints on the mathematical structure of the model. We present computational results and comparisons between ALAMO and a variety of learning techniques, including Latin hypercube sampling, simple least-squares regression, and the lasso. We also describe results from applications in CO 2 capture that motivated the development of ALAMO.

Nick Sahinidis
Department of Chemical Engineering
Carnegie Mellon University
http://archimedes.cheme.cmu.edu
Sahinidis@cmu.edu

 

Monday, November 13

3108 Etcheverry Hall, 3:30 p.m. - 5:00 p.m.

Simulation optimization problems arise in many different application areas. For example, in groundwater remediation we want to minimize the cleanup costs subject to a contamination constraint; in combustion, climate, or cosmology applications, we generally want to minimize the error between our observations and our simulations. In these applications, we have to run a computationally expensive simulation model (several minutes to hours per run) in order to obtain a single objective or constraint function value. Analytical descriptions of the objective and its derivatives are not available (black-box). The goal is to find the optimal solution within a very low number of expensive function evaluations. To this end, we use computationally cheap surrogate models to approximate the expensive simulation objective and constraint functions. Throughout the optimization, we use the surrogate models to guide the search for improved sample points at which we then query the expensive simulation. The surrogate models are updated every time a new point has been evaluated with the expensive simulation. In this talk, I will give an overview of surrogate model optimization algorithms and I will showcase application problems that we have solved successfully with this method.

Dr. Mueller is a research scientist in the Computational Research Division of the Computing Sciences Directorate at the Lawrence Berkeley National Laboratory, and affiliated with the Center for Computational Sciences and Engineering (CCSE).