Automatic scheduling method for call center based on telephone traffic prediction

Abstract

The invention discloses an automatic scheduling method for a call center based on telephone traffic prediction, and the method comprises the steps: overlapping a deep learning model and a conventionalmachine model, obtaining an integrated model, obtaining a high-precision telephone traffic prediction result through the integrated model, and correcting a telephone traffic prediction model with theconsideration to the remarkable features of the increase of the telephone traffic and a marketing factor, thereby improving the precision of the model; employing an optimized genetic algorithm to meet the complex scheduling constraints, and enabling the obtained scheduling result to be more humanized. Based on the transfer of the manpower distribution mode, the method enables a manager to be liberated from the tedious and hard scheduling work, and a fairer scheduling result enables the work enthusiasm of the employees to be further improved. The method enables an intelligent power distribution system to improve the service perception and satisfaction degree of customers.

Description

technical field [0001] The invention relates to the technical field of communications, in particular to an automatic shift scheduling method for a call center based on traffic prediction. Background technique [0002] With the quantitative management of human resources, the demand for call center traffic forecast is increasing, and the requirements for service level, agent utilization rate and working hours management are constantly improving. Under the current conditions, how to rationally arrange manpower, improve call completion rate, and optimize on-site management has become a huge challenge. A scientific and reasonable traffic forecasting model is an important basis for rationally arranging shift schedules, and an important link for call centers to achieve high-efficiency operation management, reduce operating costs, and ensure customer service quality and level. [0003] Scheduling is mainly based on the company's actual business development needs, and reasonably arr...

AI Technical Summary

Problems solved by technology

[0004] 1. The scheduling results are difficult to quantify and compare

[0005] 2. The efficiency of shift scheduling is low and time-consuming, which puts forward higher requirements on the work pressure of shift personnel

[0006] 3. Each scheduler has his own scheduling style and preference, which will cause large differences in scheduling results, which will test the self-regulation ability of employees on duty

[0007] 4. It is difficult to meet all the company's labor regulations and the wishes of most employees

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