Angela Kohlenberg

PhD Candidate, Operations Management
Northwestern University

angela.kohlenberg@kellogg.northwestern.edu



ABOUT| RESEARCH| TEACHING| TALKS

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

My research focuses on the operations of two-sided markets, where both demand and supply arrive and depart dynamically over time. Examples include organ exchange programs, ride-hailing platforms, and perishable inventory systems such as blood and food banks. My work to date examines how impatience impacts the performance and optimal control of dynamic matching markets, primarily using queueing theory.

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). Before joining the PhD program at Kellogg, I taught undergraduate and MBA courses in operations management and business analytics at the University of Alberta and Macewan University (Edmonton, Canada). Previously, I held analyst and management positions in the public sector, where I used data analytics and OM tools (e.g. forecasting, simulation, process flow analysis) to improve service delivery and decision-making related to urban planning.

I am on the 2024-2025 academic job market.


Research

Publications

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

First place, 2024 George Nicholson Student Paper Competition
First place, 2024 Canadian OR Society Queueing and Applied Probability Student Paper Competition

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

Greedy Matching of Impatient Agents: The Role of Inventory
Angela Kohlenberg
Under review
paper| slides

We study dynamic matching in two-sided markets with heterogeneous and impatient demand and supply, and demand-dependent match rewards. These markets face a trade-off between delaying matches to allow for better options and performing matches quickly to prevent agent abandonment. A policy that greedily matches agents over the entire market ignores the potential benefits of reserving inventory (waiting agents) for better future matches. However, greedily matching supply over a subset of the highest-reward demand may sacrifice the opportunity to match supply before it abandons.

Our results reveal that the optimality of greedy matching policies depends on the fundamental questions: Can, and should, excess inventory be held on the supply-side of the market? We derive precise expressions of the market parameters, which are translated into intuitive market characteristics, to answer these questions. A policy is scale optimal if its reward loss due to abandonment is on the same order as the optimal policy. We develop an algorithm for general two-sided networks that identifies whether a greedy policy is scale optimal, and, if so, specifies the optimal market design such that greedily matching agents over specified partitions of the network is scale optimal.

Work in Progress

Matching with Deteriorating Quality
with Itai Ashlagi and Itai Gurvich


Teaching

Instructor

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

Business Process Management (undergraduate elective)
University of Alberta, Winter 2020
Course evaluation cancelled due to Covid
syllabus

Operations Management (undergraduate core)
Macewan University, Winter 2020
Course evaluation cancelled due to Covid
description

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

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

Stochastic Foundations II (PhD core)
Northwestern University, Spring 2024

Service Management and Analytics (MBA elective)
Northwestern University, Winter 2024 and Winter 2023

Decision Models and Prescriptive Analytics (MBA elective)
Northwestern University, Spring 2024, Spring 2023, Winter 2023, Summer 2022, Winter 2022

Operations Management (MBA core)
Northwestern University, Summer 2024, Fall 2021

Forecasting (undergraduate elective)
University of Alberta, Winter 2018

Data Analysis and Modeling (executive MBA core)
University of Alberta, Winter 2018


Talks

Greedy Matching of Impatient Agents
INFORMS Annual Meeting 2024, Seattle, USA, October 2024
Manufacturing and Service Operations Management (MSOM) Conference, Minneapolis, USA, June 2024
Stochastic Modelling Meeting (STOCHMOD), Milan, Italy, June 2024
slides


The Cost of Impatience in Dynamic Matching
Canadian Operations Research Society (CORS) Conference, virtual talk, June 2024
Production and Operations Management Society (POMS) Conference, Minneapolis, USA, April 2024
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