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2002a), “Statistical Analysis of a Telephone Call Center: A Queueing Science Perspective,” technical report, University of Pennsylvania, downloadable at http://iew3.technion.ac.il/serveng/References/references.html
"... A call center is a service network in which agents provide telephonebased services. Customers who seek these services are delayed in telequeues. This article summarizes an analysis of a unique record of call center operations. The data comprise a complete operational history of a small banking cal ..."
Abstract

Cited by 199 (30 self)
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A call center is a service network in which agents provide telephonebased services. Customers who seek these services are delayed in telequeues. This article summarizes an analysis of a unique record of call center operations. The data comprise a complete operational history of a small banking call center, call by call, over a full year. Taking the perspective of queueing theory, we decompose the service process into three fundamental components: arrivals, customer patience, and service durations. Each component involves different basic mathematical structures and requires a different style of statistical analysis. Some of the key empirical results are sketched, along with descriptions of the varied techniques required. Several statistical techniques are developed for analysis of the basic components. One of these techniques is a test that a point process is a Poisson process. Another involves estimation of the mean function in a nonparametric regression with lognormal errors. A new graphical technique is introduced for nonparametric hazard rate estimation with censored data. Models are developed and implemented for forecasting of Poisson arrival rates. Finally, the article surveys how the characteristics deduced from the statistical analyses form the building blocks for theoretically interesting and practically useful mathematical models for call center operations.
Optimal employee retention when inferring unknown learning curves
 In Proc. 2010 Winter Simulation Conference, Edited by
, 2010
"... This paper formulates an employer’s hiring and retention decisions as an infinitearmed bandit problem and characterizes the structure of optimal hiring and retention policies. We develop approximations that allow us to explicitly calculate these policies and to evaluate their benefit. The solution ..."
Abstract

Cited by 3 (2 self)
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This paper formulates an employer’s hiring and retention decisions as an infinitearmed bandit problem and characterizes the structure of optimal hiring and retention policies. We develop approximations that allow us to explicitly calculate these policies and to evaluate their benefit. The solution involves a balance of two types of learning: the learning that reflects the improvement in performance of employees as they gain experience, and the Bayesian learning of employers as they infer properties of employees ’ abilities to inform the decision of whether to retain or replace employees. Numerical experiments with Monte Carlo simulation suggest that the gains to active screening and monitoring of employees can be substantial. 1
Corresponding author:
, 2004
"... and sponsored research. 1 A call center is a service network in which agents provide telephonebased services. Customers that seek these services are delayed in telequeues. This paper summarizes an analysis of a unique record of call center operations. The data comprise a complete operational histo ..."
Abstract
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and sponsored research. 1 A call center is a service network in which agents provide telephonebased services. Customers that seek these services are delayed in telequeues. This paper summarizes an analysis of a unique record of call center operations. The data comprise a complete operational history of a small banking call center, call by call, over a full year. Taking the perspective of queueing theory, we decompose the service process into three fundamental components: arrivals, customer patience, and service durations. Each component involves different basic mathematical structures and requires a different style of statistical analysis. Some of the key empirical results are sketched, along with descriptions of the varied techniques required.
Optimal Hiring and Retention Policies for Heterogeneous Workers who Learn
"... We study the hiring and retention of heterogeneous workers who learn over time. We show that the problem can be analyzed as an infinitearmed bandit with switching costs and apply results from Bergemann and Välimäki (2001) to characterize the optimal hiring and retention policy. For problems with Ga ..."
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We study the hiring and retention of heterogeneous workers who learn over time. We show that the problem can be analyzed as an infinitearmed bandit with switching costs and apply results from Bergemann and Välimäki (2001) to characterize the optimal hiring and retention policy. For problems with Gaussian data, we develop approximations that allow the efficient implementation of the optimal policy and the evaluation of its performance. Our numerical examples demonstrate that the value of active monitoring and screening of employees can be substantial.
Essays in Problems of Optimal Sequential Decisions
"... In this dissertation, we study several Markovian problems of optimal sequential decisions by focusing on research questions that are driven by probabilistic and operationsmanagement considerations. Our probabilistic interest is in understanding the distribution of the total reward that one obtains ..."
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In this dissertation, we study several Markovian problems of optimal sequential decisions by focusing on research questions that are driven by probabilistic and operationsmanagement considerations. Our probabilistic interest is in understanding the distribution of the total reward that one obtains when implementing a policy that maximizes its expected value. With this respect, we study the sequential selection of unimodal and alternating subsequences from a random sample, and we prove accurate bounds for the expected values and exact asymptotics. In the unimodal problem, we also note that the variance of the optimal total reward can be bounded in terms of its expected value. This fact then motivates a much broader analysis that characterizes a class of Markov decision problems that share this important property. In the alternating subsequence problem, we also outline how one could be able to prove a Central Limit Theorem for the number of alternating selections in a finite random sample, as the size of the sample grows to infinity. Our operationsmanagement interest is in studying the interaction of onthejob learning and learningbydoing in a workforcerelated problem. Specifically, we study the sequential hiring and retention of heterogeneous workers who learn over time. We model the hiring and retention problem as a Bayesian infinitearmed bandit, and we characterize the optimal policy in detail. Through an extensive set of numerical examples, we gain
OPTIMAL EMPLOYEE RETENTION WHEN INFERRING UNKNOWN LEARNING CURVES
"... This paper formulates an employer’s hiring and retention decisions as an infinitearmed bandit problem and characterizes the structure of optimal hiring and retention policies. We develop approximations that allow us to explicitly calculate these policies and to evaluate their benefit. The solution ..."
Abstract
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This paper formulates an employer’s hiring and retention decisions as an infinitearmed bandit problem and characterizes the structure of optimal hiring and retention policies. We develop approximations that allow us to explicitly calculate these policies and to evaluate their benefit. The solution involves a balance of two types of learning: the learning that reflects the improvement in performance of employees as they gain experience, and the Bayesian learning of employers as they infer properties of employees ’ abilities to inform the decision of whether to retain or replace employees. Numerical experiments with Monte Carlo simulation suggest that the gains to active screening and monitoring of employees can be substantial. 1