The Modeling of Call Center Functioning in Case of Overload

Stepanov, S. N.; Stepanov, Mikhail S.; Zhurko, H M

doi:10.1007/978-3-030-36614-8_30

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…It was approached with methods of queuing theory in (Bouchentouf, Cherfaoui, and Boualem 2021) (OR), with deep neural networks and reinforcement learning in (Kumwilaisak et al 2022) (RL), and various other methods (Cohen, Reis, and Amorim 2020) (OR), (Horng and Lin 2020) (informatics, social science). Allocation of tasks in cases where there are operators with two levels of expertise, as our model may be described, is discussed in (Stepanov, Stepanov, and Zhurko 2019) (OR).…”

Section: Customer Service Center Managementmentioning

confidence: 99%

Customer Service Combining Human Operators and Virtual Agents: A Call for Multidisciplinary AI Research

Kraus

Oshrat

Aumann

et al. 2023

AAAI

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The use of virtual agents (bots) has become essential for providing online assistance to customers. However, even though a lot of effort has been dedicated to the research, development, and deployment of such virtual agents, customers are frequently frustrated with the interaction with the virtual agent and require a human instead. We suggest that a holistic approach, combining virtual agents and human operators working together, is the path to providing satisfactory service. However, implementing such a holistic customer service system will not, and cannot, be achieved using any single AI technology or branch. Rather, such a system will inevitably require the integration of multiple and diverse AI technologies, including natural language processing, multi-agent systems, machine learning, reinforcement learning, and behavioral cloning; in addition to integration with other disciplines such as psychology, business, sociology, economics, operation research, informatics, computer-human interaction, and more. As such, we believe this customer service application offers a rich domain for experimentation and application of multidisciplinary AI. In this paper, we introduce the holistic customer service application and discuss the key AI technologies and disciplines required for a successful AI solution for this setting. For each of these AI technologies, we outline the key scientific questions and research avenues stemming from this setting. We demonstrate that integrating technologies from different fields can lead to a cost-effective successful customer service center. The challenge is that there is a need for several communities, each with its own language and modeling techniques, different problem-solving methods, and different evaluation methodologies, all of which need to work together. Real cooperation will require the formation of joint methodologies and techniques that could improve the service to customers, but, more importantly, open new directions in cooperation of diverse communities toward solving joint difficult tasks.

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Section: Customer Service Center Managementmentioning

confidence: 99%

Customer Service Combining Human Operators and Virtual Agents: A Call for Multidisciplinary AI Research

Kraus

Oshrat

Aumann

et al. 2023

AAAI

View full text Add to dashboard Cite

show abstract

“…In teletraffic papers and articles theoretical analysis has been done for some of the mentioned above problems [1][2][3][4][5][6][7][8][9][10][11][12][13][14], including the studying of retrials [5][6][7][8], overloads in call centers [4,[13][14], the study of possibility of waiting and the investigation of dependence of the request servicing on the operators' skill levels [1][2][3]. In this paper all mentioned factors are considered together.…”

Section: Introductionmentioning

confidence: 99%

The construction and analysis of call-center model in overload traffic condition

Stepanov¹,

Mtuci²,

Shishkin³

et al. 2020

T-Comm

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The functional and mathematical models of call center working in case of overload are constructed and analyzed. In the model the following features are considered: the possibility of serving of coming request by IVR (Interactive Voice Response); the option of waiting the beginning of service in case of blocking and the opportunity of request repetition in case of occupation of all waiting positions or unsuccessful finishing of waiting time. Markov process that describes model functioning is defined. Main performance measures of requests coming and serving are given with help of values of stationary probabilities of model's states. The values of performance measures are found after solving the system of state equations by Gauss-Zeidel iterative approach. Expressions that relates the model's main performance measures in form of local and global conservation laws are found. The obtained results can be used for indirect measurement of intensity of primary requests and the probability of call repetition. It is shown how to use the model and the derived results for reducing the negative effects of overload by filtering the input flows of primary and repeated attempts. The usage of the model for calculation of the numbers of operators and waiting places required to serve the incoming traffic flows with given value of probability of call losses and mean value of waiting the beginning of service is considered. Numerical results that illustrate the implementation of the derived expressions and algorithms are given.

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Performance Measures of Emergency Services in Case of Overload

Stepanov

Shishkin

2020

Distributed Computer and Communication Networks

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The Modeling of Call Center Functioning in Case of Overload

Cited by 7 publications

References 16 publications

Customer Service Combining Human Operators and Virtual Agents: A Call for Multidisciplinary AI Research

Customer Service Combining Human Operators and Virtual Agents: A Call for Multidisciplinary AI Research

The construction and analysis of call-center model in overload traffic condition

Performance Measures of Emergency Services in Case of Overload

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