Dynamic Scheduling of a Multiclass Queue in the Halfin-Whitt Regime: A Computational Approach for High-Dimensional Problems

• 成果类型: Article
• 署名作者: Ata, Baris; Kasikaralar, Ebru
• 署名单位: University of Chicago
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.03921
• 发表日期: 2026
• 关键词: call center management; Deep learning; queues: diffusion models

摘要

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.