Prof. Shane G. HENDERSON

Charles W. Lake, Jr. Professor in Productivity
School of Operations Research and Information Engineering
Cornell University

Topic: Modeling the Impact of Community First Responders

Abstract:
Patient survival from out-of-hospital cardiac arrest (OHCA) can be improved by augmenting traditional ambulance response with the dispatch of community first responders (volunteers) who are alerted via an app. How many volunteers are needed, from where should volunteers be recruited, and how should they be dispatched? We use a combination of Poisson point process modeling and convex optimization to address the first two questions; the right areas from which to recruit are not always obvious, because volunteers recruited from one area may spend time in various areas across a city. To answer the third question we use a combination of dynamic programming and decision trees, balancing the goal of a fast response to the current patient with the need to avoid disengagement of volunteers that arises when multiple volunteers respond. A case study for Auckland, New Zealand demonstrates the ideas.

This is joint work with Pieter van den Berg, Océane Fourmentraux, Caroline Jagtenberg, and Hemeng (Maggie) Li

Prof. Ming HU

Distinguished Professor of Business Operations and Analytics
Rotman School of Management
University of Toronto

Title: Spatial Supply-Demand Matching 

Abstract: 
First, I will model recent applications in the on-demand economy as spatial queues to extend the scope of urban operations research and draw managerial insights. Second, I will develop theories on a transient spatial congestion system and general spatial resource allocation problems.

Prof. Peng SUN

J.B. Fuqua Professor
Fuqua School of Business
Duke University

Topic: Equilibrium with Communication, Information Design, and Linear Programming

Abstract: 
Linear programming and its duality provide analytical tools to study games with communication and information design. After reviewing the basic concepts of equilibrium with communication, we study two applications. In the first application, we show that in simple environments, a bidding ring operating at a first price sealed-bid auction cannot achieve any gains relative to non-cooperative bidding if the ring is unable to control the bids that its members submit at the auction. In the second application, we study a Cournot competition model in which players are able to share information about the uncertain market potential. We show that there is no incentive for the players to fully share their information. On the other hand, whether we want to maximize the collective gain of the firms, or the social welfare, the non-cooperative equilibrium (no information sharing) can also substantially underperform, compared with upper bounds. When there is much room for improvement over no information sharing, we propose a mechanism which shares an information mediator’s information as well as partially sharing each firm’s information using a threshold approach.

Prof. Assaf ZEEVI

Kravis Professor of Business
Columbia University

Topic: Some Flavors of Statistical Learning in Operations

Abstract: 
As data becomes increasingly abundant, accessible, and a major driver of economic activity, significant attention has focused on machine learning tools for addressing a variety of data-driven decision making problems, including variations on traditional operations research problems. In this talk we will present some vignettes of research questions that involve ideas and tools from statistical learning that are blended into operations-type settings. The talk will highlight some concepts that are relevant both to problem formulation as well as solution methodology, and draw some (hopefully interesting) connections to practical problem settings and application domains.