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呼叫中心排班优化模型的研究
引用本文:徐迅羽,杨根科.呼叫中心排班优化模型的研究[J].微型电脑应用,2012,28(6):55-61.
作者姓名:徐迅羽  杨根科
作者单位:上海交通大学,上海,200240
摘    要:呼叫中心排班系统的优化研究是在话务员和坐席资源约束下,根据服务需求时间序列,合理安排资源,以提高呼叫接入服务质量、提高话务员的个性化满意度和降低运营成本的多目标复杂约束的优化问题。根据客服质量与话务员满意度相关的指标和约束,引入了服务水平、排班成本、话务员疲劳度、排班公平性和话务员意愿等因素。基于对满足服务水平的班组数量的预测,依据对外的规划经营和对内的管理调度的区别,将排班决策分为班次设计和班组指派两大具有递进关系的子模型。利用改进型PSO算法和旋转排班法进行求解,并且根据排班结果和评价参数对规划调度参数进行反馈调整。最后通过仿真分析说明了本文方法在兼顾客服满意度和话务员满意度的呼叫中心排班决策系统中有很好的适应性。

关 键 词:排班优化问题  服务参数预测  设计与指派模型  改进型PSO

Optimal Staff Scheduling Model in Call Center System
Xu Xunyu , Yang Genke.Optimal Staff Scheduling Model in Call Center System[J].Microcomputer Applications,2012,28(6):55-61.
Authors:Xu Xunyu  Yang Genke
Affiliation:(Shanghai Jiao Tong University,Shanghai200240,China)
Abstract:The call center scheduling optimization research is to implement the resource arrangement under the staff constraints in service time-series in order to improve the quality of call-in service,improve the staff’s personal satisfaction and reduce operating costs.It is regarded as a complex multi-objective constrained optimization problem.According to quality indicators and related con-straints relative to the standard of service as well as staff satisfaction,the following factors are introduced including the standard of service,scheduling cost,staff fatigue,scheduling fairness and staff’s personal will.First,based on service parameters were forecasted and difference between external operation and internal management,the scheduling process is divided into two sub-models: group design model and group assignment model.Then improved PSO algorithm and shifting scheduling method are adopted to solve the problem respectively.Feedback adjustments are implemented for operation and management parameter according to the scheduling result.Finally,through simulation,final scheduling table can be gained which indicates that the method giving consideration to the satisfaction of both the customers and the staff and is charactered with good adaptability in call center scheduling decision-making system.
Keywords:Staff Scheduling Optimization  Data Forecast  Design and Assignment Model  Improved PSO
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