Amy Ward's research focuses on the approximation and control of stochastic systems, with applications to the service industry. Much of her past work has focused on the impact of customer impatience and abandonments on performance. Her recent work investigates the interactions between behavioral incentives and operational efficiency in service systems, with a focus on call centers. Please view her personal web site for a list of her published papers.
Ward is the chair of the Applied Probability Society (term 11/2016-11/2018) and is the Service Special Interest Group (SIG) Chair for the MSOM Society (term 6/2017-6/2019). She serves as the Stochastic Models Area Editor for the journal Operations Research.
Prior to joining Booth, Ward was Professor of Data Sciences and Operations at the University of Southern California Marshall School of Business. She has also been a Visiting Associate Professor in the Computing and Mathematical Sciences Department at Cal Tech, and an Assistant Professor in Industrial and Systems Engineering at the Georgia Institute of Technology. Outside of academia, during her doctoral studies, she spent several summers at AT&T Laboratories.
Ward earned both a PhD and MA from Stanford University, and she holds a BA from Claremont McKenna College.
REVISION: Dynamic Matching for Real-Time Ridesharing
In a ridesharing system such as Uber or Lyft, arriving customers must be matched with available drivers. These decisions affect the overall number of customers matched, because they impact whether or not future available drivers will be close to the locations of arriving customers. A common policy used in practice is the closest driver (CD) policy that 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 achieve better performance.
We propose matching policies based on a continuous linear program (CLP) that accounts for (i) the differing arrival rates of customers and drivers in different areas of the city, (ii) how long customers are willing to wait for driver pick-up, and (iii) the time-varying nature of all the aforementioned parameters. We prove asymptotic optimality of a forward-looking CLP-based policy in a large market regime. We also prove the asymptotic ...
REVISION: Incentive Based Service System Design: Staffing and Compensation to Trade Off Speed and Quality
Most common queueing models used for service system design assume the servers work at fixed (possibly heterogeneous) rates. However, real-life service systems are staffed by people, and people may change their service speed in response to their compensation incentives. The delicacy is that the resulting employee service rate affects the staffing, but also the staffing affects the resulting employee service rate. Our objective in this paper is to find a joint staffing and compensation policy that induces optimal service system performance.
We do this under the assumption that there is a trade-off between service speed and quality, and employees are paid based on both. The employees each selfishly choose their own service speed in order to maximize their own expected utility (which depends on the staffing through their busy time). We prove the existence of an equilibrium service speed under a simple piece-rate compensation policy, and show the convergence to a unique limit as the ...
REVISION: Dynamic Scheduling in a Many-Server Multi-Class System: The Role of Customer Impatience in Large Systems
Problem Definition: We study optimal scheduling of customers in service systems, such as call centers. In such systems, customers typically hang up and abandon the system if their wait for service is too long. Such abandonments are detrimental for the system, and so managers typically use scheduling as a tool to mitigate it. In this paper we study the interplay between customer impatience and scheduling decisions when managing heterogeneous customer classes.
Academic/Practical Relevance: Call centers constitute a large industry, that has a global spending of around $300 billion, and employs more than 15 million people worldwide. Our work focuses on improving call center operations which can reduce costs and improve customer satisfaction. Mathematically, customer patience is typically modeled as exponentially distributed for tractability. Our work makes inroads into relaxing this restrictive assumption to allow modeling more realistic call center situations.
Methodology: We use ...