Cluster Computing

, Volume 22, Supplement 3, pp 5207–5215 | Cite as

Multi-attribute intelligent queueing method for onboard call centers

  • Xuehua SunEmail author


The present research proposes a multi-attribute onboard call center intelligent queueing method applicable to the vehicle service field. In the previous studies concerning the call center queueing methods, the characteristics associated with the incoming call customers include the impatient waiting and the notification of waiting time, etc; however, the present paper takes into consideration a suite of additional customer attributes, i.e., emergency calls from intelligent onboard terminals, whether the call requests are from the area covered by the company’s 4S service, whether the call waiting time stays within a reasonable range, and whether there exists an necessity for engaging the third party service system. Also, priority classification is conducted based on the multiple attributes associated with the aforementioned customers. Besides, to optimize the service structure of the agent service representatives, the present study divides the agent service representatives into two groups, namely professional skill group and regular skill group. This allows the processing of call business to be carried out in two different modes. Correspondingly, preset processing time tables for the call business are established for regular and professional skill groups, respectively, so as to enable the service of agent representatives to better distinguish between regular business and complicated business, and conduct suitable processing accordingly. As a result, the processing efficiency is reinforced, the customers’ waiting time is reduced, and consequently the total processing time of agent service representatives is minimized. The research methodology adopted in the present manuscript is highly applicable to the design and management of call centers.


Intelligent onboard call center Call feature classification Priority queue set Multi-attribute queueing method 



This work is supported by Shanghai Science and Technology Committee (No. 12dz1124300).


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Shanghai Key Lab Intelligent Mfg & RobotShanghai UniversityShanghaiChina

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