A Multi-Constraint Scheduling Method for Call Center

Abstract

The invention discloses a multi-constraint scheduling method for a call center. The method first determines the shift situation of the next day from the shift of the previous day according to shift rotation rules, and then screens out the shift from the shift of the next day according to other constraints. For suitable shifts, through the search of the solution space of the scheduling results, continuous screening and pruning are carried out to find out the most qualified shift situation, and so on, until the shift situation of the entire cycle is formed, and then each The shift of the group is adjusted to form a reasonable commuting time, more people are arranged during the peak period of traffic, and fewer people are arranged during the low period of traffic, finally forming a reasonable shift situation for the entire cycle; the present invention It is suitable for situations where the constraint conditions are very complicated, and can better solve the scheduling problem of the call center; it can ensure the comfort of employees while ensuring the connection rate, and effectively solve the scheduling problem of the call center.

Description

technical field [0001] The invention belongs to the field of network communication, in particular to a multi-constraint scheduling method for a call center. Background technique [0002] A call center is a department that provides various telephone services to customers. For example, the call centers of taxis, the call centers of telecom operators, and the after-sales service hotlines of various companies are all call centers. [0003] The traffic volume is the number of incoming calls. The traffic volume of the call center is different at each time of the day. If the staff’s commuting time is set to be fixed, it will lead to insufficient staff during the peak traffic period and low traffic volume. Due to the overflow of the number of people during the period, the completion rate cannot be well satisfied. Therefore, it is necessary to arrange more staff when the traffic volume is high, arrange fewer staff when the traffic volume is low, and dynamically adjust the commuting...

AI Technical Summary

Problems solved by technology

However, when there are many and complex constraints, the solution obtained by the genetic algorithm deviates greatly from the optimal solution, and many constraints will be broken, and even the solution cannot be obtained.

It leads to the need for human intervention in the scheduling results, or even re-scheduling, reducing work efficiency

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