Ebru Kasikaralar

We consider a multiclass 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. [Beck C, Becker S, Cheridito P, Jentzen A, Neufeld A (2021) Deep splitting method for parabolic PDEs. SIAM J. Sci. Comput. 43(5):A3135–A3154], 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 (prelimit) 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.

The market for solid-state lighting (SSL) systems has expanded 40-fold in installed lamps since 2001. At the same time, systems which preserve materials over time and promote material reuse are getting increasing attention in light of calls for reducing consumption of natural resources. As new lighting technology products are designed and brought to market, consideration must be given to how products will be managed throughout the life-cycle as well as their end-of-life (EOL) fate. Lighting-as-a-service (LaaS) business models have emerged as a potential strategy for preserving the materials embedded in lighting products. In this paper, we examine the cost and environmental implications of technology management decisions in the context of the street lighting industry, employing life-cycle assessment and a Markov Decision Process model. The goal of the research is to determine a policy that minimizes expected costs and emissions for the system over a fixed time horizon thus reducing uncertainty for managers. The model used in the paper evaluates the optimal replacement strategies for street lighting products and additionally connects the result to the optimal EOL product trajectory, taking both costs and carbon emissions into account. In doing so, we are able to more deeply understand the role that LaaS business models might play in enabling closed-loop systems within the street lighting industry.

Motivated by the skill-based routing problem commonly encountered in call centers, we study the dynamic control of a parallel-server queueing system. 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. 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 (pre-limit) 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 from the literature. Across the test problems considered so far, our policy performs at least as well as 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.

Advances in storing and processing big data have transformed how digital platforms learn about product quality and user preferences. Since collecting and using data is essential in generating successful matching algorithms and enhancing customers' purchase experience, platforms, and companies heavily rely on consumer data to make pricing decisions accordingly. On the one hand, due to increased data privacy concerns, policies restricting firms' access to consumers' data are expected to become more prevalent. On the other hand, restricting the firms' access to consumer data negatively affects not only the firms but also the consumers since personalization is lost. Instead, considering giving ownership to the consumers of their data would give them control over how their data is used/collected. Consequently, with the consumers' full ownership of their data, we introduce a model where the myopic consumers and a monopolistic firm directly interact in two different markets: product and data markets. There is a costly dynamic data exchange between the firm and the consumers, where the firm offers incentives to buyers to sell their data. The firm uses acquired data from consumers to learn the underlying product quality, and consumers use the acquired data from the firm to make strategic purchase decisions.