Ebru Kasikaralar

Baris Ata, Ebru Kasikaralar

We consider a multi-class queueing model of a telephone call center, in which a system manager dynamically allocates available servers to customer calls. Calls can terminate through either service completion or customer abandonment, and the manager strives to minimize the expected total of holding costs plus abandonment costs over a finite horizon. Focusing on the Halfin-Whitt heavy traffic regime, we derive an approximating diffusion control problem, and building on earlier work by Beck et al. (2021), develop a simulation-based computational method for solution of such problems, one that relies heavily on deep neural network technology. Using this computational method, we propose a policy for the original (pre-limit) call center scheduling problem. Finally, the performance of this policy is assessed using test problems based on publicly available call center data. For the test problems considered so far, our policy does as well as or better than the best benchmark we could find. Moreover, our method is computationally feasible at least up to dimension 500, that is, for call centers with 500 or more distinct customer classes.

Baris Ata, Ebru Kasikaralar

A key operational challenge for call centers is to decide, in real time, which waiting customer should be served by which available agent. This is known as skill-based routing, and the decision becomes especially difficult in large systems with many customer classes, where standard dynamic programming methods can be computationally intractable. Focusing on the Halfin-Whitt heavy-traffic regime and an infinite-horizon discounted cost criterion, we develop a computational method that scales to high-dimensional settings with many customer classes. Our approach begins by deriving an approximating diffusion control problem in the heavy traffic limiting regime. Building on earlier work by Han et al. (2018), we develop a simulation-based method to solve this problem, relying heavily on deep neural network techniques. Using this framework, we construct a policy for the original (prelimit) call center scheduling problem. To evaluate performance, we adopt a data-driven approach. Using call center data from a large U.S. bank, we calibrate the model and construct realistic test instances. We then compare the resulting policy with benchmark policies drawn from the literature. Across all test problems considered so far, our policy performs at least as well as or better than the best benchmark identified. Moreover, the method remains computationally feasible in dimensions up to 100, corresponding to call centers with 100 or more distinct customer classes.