Angela Kohlenberg

PhD Candidate, Operations Management
Northwestern University

angela.kohlenberg@kellogg.northwestern.edu

RESEARCH| TEACHING| TALKS| ABOUT

I am a fourth year PhD candidate in Operations Management at the Kellogg School of Management, Northwestern University. I am advised by Professor Itai Gurvich.

My research focuses on understanding how impatience impacts the performance and control of dynamic matching markets. This work is motivated by matching decisions that arise in organ exchange/allocation, ride-sharing, and perishable inventory allocation (e.g. blood banks and food banks).

Publications

The Cost of Impatience in Dynamic Matching: Scaling Laws and Operating Regimes + abstract
Angela Kohlenberg and Itai Gurvich
Management Science, forthcoming
paper| slides

We study matching queues with abandonment. The simplest of these is the two-sided queue with servers on one side and customers on the other, both arriving dynamically over time and abandoning if not matched by the time their patience elapses. We identify non-asymptotic and universal scaling laws for the matching loss due to abandonment, which we refer to as the "cost-of-impatience." The scaling laws characterize the way in which this cost depends on the arrival rates and the (possibly different) mean patience of servers and customers.

Our characterization reveals four operating regimes identified by an operational measure of patience that brings together mean patience and utilization. The four regimes subsume the regimes that arise in asymptotic (heavy-traffic) approximations. The scaling laws, specialized to each regime, reveal the fundamental structure of the cost-of-impatience and show that its order-of-magnitude is fully determined by (i) a "winner-take-all" competition between customer impatience and utilization, and (ii) the ability to accumulate inventory on the server side. Practically important is that when servers are impatient, the cost-of-impatience is, up to an order-of-magnitude, given by an insightful expression where only the minimum of the two patience rates appears.

Considering the trade-off between abandonment and capacity costs, we characterize the scaling of the optimal safety capacity as a function of costs, arrival rates, and patience parameters. We prove that the ability to hold inventory of servers means that the optimal safety capacity grows logarithmically in abandonment cost and, in turn, slower than the square-root growth in the single-sided queue.

Working Papers

Quality Versus Quantity in Dynamic Matching with Impatient Agents + abstract
Angela Kohlenberg
Submitted April 2024
paper

We study the trade-off between match quality and quantity in dynamic matching with impatient agents. In these matching markets, delaying matches may allow for better options to become available as more agents arrive, but can also lead to lost matches as agents depart. Matching decisions must balance the competing objectives of higher-quality matches and a greater number of matches.

We consider a two-sided matching market with impatient, heterogeneous agents and matches that are either high-quality (high reward) or low-quality (low reward). We show that the optimal balance between match quality and quantity is determined by the amount of waiting agents (Inventory Imbalance) and the “cost” of waiting (Reward-Loss Ratio) on each side of the market. A matching market operates in one of four regimes based on informative expressions of the Inventory Imbalance and/or Reward-Loss Ratio.

When excess inventory cannot accumulate on the short side of the market, the market is in either the Quantity-Driven, Quality-Driven, or Flexible regime, based only on the mean patience of each side: either a greedy policy (Quantity-Driven and Flexible) or dedicated policy that only performs high-quality matches (Quality-Driven) is near-optimal. When the Inventory Imbalance is large relative to the Reward-Loss Ratio, the market is the Quantity-Driven regime and a greedy policy is near-optimal. When the Inventory Imbalance is very small relative to the Reward-Loss Ratio, the market is in the Quality-Driven regime and a dedicated policy is near-optimal. Otherwise, the market is the Balanced regime and low-quality matches should only be performed when there is enough inventory on the short side.

Instructor

OM 502: Operations Management
MBA elective, University of Alberta, Spring 2020 and Summer 2018
Mean Overall Instructor (2020): 4.5/5.0 (32 students)
syllabus

OM 411: Business Process Management
Undergraduate elective, University of Alberta, Winter 2020
Course evaluation cancelled due to Covid
syllabus

MGTS 352: Operations Management
Undergraduate core, Macewan University, Winter 2020
Course evaluation cancelled due to Covid
description

MGTS 113: Introduction to Quantitative Decision Making
Undergraduate core, Macewan University, Winter 2020 and Fall 2018
Mean Overall Instructor (2018): 4.6/5.0 (36 students)
description

Lab Instructor and Manager

MGTSC 501: Data Analysis and Decision Making
MBA core, University of Alberta, Fall 2018 and Fall 2017
Mean Overall Instructor (2018): 4.6/5.0 (121 students)
syllabus

Teaching Assistant

OPNS 912: Service Management and Analytics
MBA elective, Northwestern University, Winter 2024 and Winter 2023

OPNS 450: Decision Models and Prescriptive Analytics
MBA elective, Northwestern University, Spring 2023, Winter 2023, Summer 2022, Winter 2022

OPNS 430: Operations Management
MBA core, Northwestern University, Fall 2021

MGTSC 405: Forecasting
Undergraduate elective, University of Alberta, Winter 2018

MGTSC 820: Data Analysis and Modeling
Executive MBA core, University of Alberta, Winter 2018

The Cost of Impatience in Dynamic Matching
INFORMS Annual Meeting 2023, Phoenix, USA, October 2023
Applied Probability Society (APS) Conference, Nancy, France, June 2023
INFORMS Annual Meeting 2022, Indianapolis, USA, October 2022
slides

I hold a bachelor’s degree in Operations Management from the University of Alberta (Edmonton, Canada) and an MBA from the Schulich School of Business, York University (Toronto, Canada). Prior to joining the PhD program at Kellogg, I was teaching undergraduate and graduate courses in Operations Management at the University of Alberta and Macewan University (Edmonton, Canada). Previously, I held analyst and management positions in the public sector, where I was primarily responsible for using data analytics and OM tools (i.e. forecasting, simulation, process flow analysis) to improve service delivery and decision-making related to urban planning and land development.