Amy Ward

About

My main interest is in service operations management; see [this explanation](http://review.chicagobooth.edu/strategy/2019/article/amy-r-ward-says-customers-are-getting-impatient) in the Chicago Booth Review. Services encompass a broad and diverse range of industries including airlines, hospitals, call centers, and online marketplaces. The importance of promoting efficient operations in service firms is largely due to the fact that service firms provide most of the GDP and employment in post-industrial economies such as the U.S. Service firms cannot predict either when customers will arrive or how long the processing of each customer will take. Even when customers schedule appointments (such as to visit a physician), the service provider must deal with early and late arrivals, cancellations, and no-shows. In contrast to firms that produce products, service firms cannot build up inventory in order to buffer themselves from unexpected bursts in customer arrivals. Hence service firms prior

Research topics

• Computer Science
• Marketing
• Machine Learning
• Economics
• Data Mining
• Engineering
• Business
• Operations research
• Artificial Intelligence
• Mathematical optimization

Selected publications

• Dynamic Matching for Real-Time Ride Sharing

  Stochastic Systems · 2020 · 236 citations

  Senior authorCorresponding

  In a ride-sharing system, arriving customers must be matched with available drivers. These decisions affect the overall number of customers matched, because they impact whether future available drivers will be close to the locations of arriving customers. A common policy used in practice is the closest driver policy, which offers an arriving customer the closest driver. This is an attractive policy because it is simple and easy to implement. However, we expect that parameter-based policies can a…

  DOI

• Matching Impatient and Heterogeneous Demand and Supply

  Operations Research · 2024-05-15 · 20 citations

  articleSenior author

  Balancing Speed and Value in On-Demand Matching Platforms In “Matching Impatient and Heterogeneous Demand and Supply,” Aveklouris, DeValve, Stock, and Ward consider a fundamental trade-off faced by many platforms (e.g., Uber/Lyft) that match supply (e.g., drivers) and demand (e.g., riders) dynamically over time: making matches quickly capitalizes on the value of current supply and demand in the system, whereas waiting may enable better matches at the risk of losing impatient customers. They show…

  PublisherDOI

• Impact of Task-Level Worker Specialization, Workload, and Product Personalization on Consumer Returns

  Manufacturing & Service Operations Management · 2020 · 19 citations

  Senior authorCorresponding

  Problem definition: Are consumer product returns largely a function of retailers’ return policies, or can manufacturers influence them through production characteristics and product attributes? How...

  DOI

• Data-Driven Market-Making via Model-Free Learning

  2020 · 16 citations

  Senior authorCorresponding

  This paper studies when a market-making firm should place orders to maximize their expected net profit, while also constraining risk, assuming orders are maintained on an electronic limit order book (LOB). To do this, we use a model-free and off-policy method, Q-learning, coupled with state aggregation, to develop a proposed trading strategy that can be implemented using a simple lookup table. Our main training dataset is derived from event-by-event data recording the state of the LOB. Our propo…

  DOI

• Behavior-Aware Queueing: The Finite-Buffer Setting with Many Strategic Servers

  Operations Research · 2023-07-13 · 10 citations

  articleOpen accessSenior author

  In “Behavior-Aware Queueing: The Finite-Buffer Setting with Many Strategic Servers,” Zhong, Gopalakrishnan, and Ward develop a game-theoretic many-server Markovian queueing model with finite or infinite buffers to study the behavior of strategic servers whose choice of work speed depends on managerial decisions regarding (i) how many servers to staff and how much to pay them and (ii) whether and when to turn away customers. In order to predictably control system performance (e.g., lost demand, c…

  PublisherOA PDFDOI

Frequent coauthors

• Harsha Honnappa

  13 shared

• Dongyuan Zhan

  University College London

  12 shared

• Rahul Jain

  University of Southern California

  11 shared

• Chihoon Lee

  Stevens Institute of Technology

  10 shared

• Erica L. Plambeck

  10 shared

• Josh Reed

  New York University

  8 shared

• Yueyang Zhong

  University of Chicago

  8 shared

• Peter W. Glynn

  7 shared

Education

• Ph.D., Management Science and Engineering

  Stanford

  2001

Awards & honors

• Fellow of the INFORMS Manufacturing and Service Operations M…

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• Ozan Candoganh-19
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