Open Access

Dynamic Operator Allocation for Conversational AI Voice-Calling Systems

https://doi.org/10.55640/ijaair-v03i07-07

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Nadezhda Shiroglazova ¹

⁴ Northwestern University Chicago, USA

Abstract

The article examines the dynamic allocation of outbound call launches in conversational AI voice-calling systems, where voice bots qualify customers before transferring them to human operators. The study aims to reduce customer hold time and operator idleness by replacing fixed-concurrency settings with a receding-horizon integer-programming controller. The relevance of the work stems from the growing use of AI voice agents in outbound contact centers, where stochastic answer behavior, delayed transfers, and scarce operator capacity create a control problem that static dialing rules cannot resolve. The novelty lies in modeling the bot-induced transfer delay as an empirical lag convolution and embedding this representation within a closed-loop optimization framework, validated through discrete-event simulation calibrated against production logs. The main results show that the proposed controller captures most of the throughput from aggressive dialing while reducing abandonment and hold times, and it unlocks unused operator capacity at higher staffing levels. The article will be useful to researchers, contact center engineers, operations managers, and developers of AI voice-calling platforms.

Keywords

outbound call center, predictive dialing, integer programming, receding-horizon control, discrete-event simulation, AI voice agents

References

Hong, G.-Y., & Chen, X.-Y. (2025). Simulation optimization for queues with heavy-tailed service times. Journal of the Operations Research Society of China, 13(3), 810–836. https://doi.org/10.1007/s40305-024-00562-z

Köhler, J., Soloperto, R., Müller, M. A., & Allgöwer, F. (2021). A computationally efficient robust model predictive control framework for uncertain nonlinear systems. IEEE Transactions on Automatic Control, 66(2), 794–801. https://doi.org/10.1109/TAC.2020.2982585

Koole, G. M., & Li, S. (2023). A practice-oriented overview of call center workforce planning. Stochastic Systems, 13(4), 479–495. https://doi.org/10.1287/stsy.2021.0008

Rahimian, H., & Pagnoncelli, B. (2023). Data-driven approximation of contextual chance-constrained stochastic programs. SIAM Journal on Optimization, 33(3), 2248–2274. https://doi.org/10.1137/22M1528045

Ramírez-Pico, C., Ljubić, I., & Moreno, E. (2023). Benders adaptive-cuts method for two-stage stochastic programs. Transportation Science, 57(5), 1252–1275. https://doi.org/10.1287/trsc.2022.0073

Schwenkel, L., Köhler, J., Müller, M. A., & Allgöwer, F. (2023). Model predictive control for linear uncertain systems using integral quadratic constraints. IEEE Transactions on Automatic Control, 68(1), 355–368. https://doi.org/10.1109/TAC.2022.3171410

Ta, T. A., Chan, W., Bastin, F., & L'Ecuyer, P. (2021). A simulation-based decomposition approach for two-stage staffing optimization in call centers under arrival rate uncertainty. European Journal of Operational Research, 293(3), 966–979. https://doi.org/10.1016/j.ejor.2020.12.049

Wang, L., Huang, N., Hong, Y., Liu, L., Guo, X., & Chen, G. (2023). Voice-based AI in call center customer service: A natural field experiment. Production and Operations Management, 32(4), 1002–1018. https://doi.org/10.1111/poms.13953

Werner, C. (2021). Assessing and modelling tail dependencies between service times in discrete event simulation with minimum information copulas for a better understanding of maximum time in system risk. Frontiers in Applied Mathematics and Statistics, 7, Article 641245. https://doi.org/10.3389/fams.2021.641245

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International Journal of Advanced Artificial Intelligence Research

Dynamic Operator Allocation for Conversational AI Voice-Calling Systems

Abstract

Keywords

References

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