分阶段调度优化提升客服中心排班效率与准确性
Optimized Agent Shift Scheduling Using Multi-Phase Allocation Approach
- 将排班问题拆分为天数与班次两阶段求解，降低计算复杂度
- 在节假日高峰场景下，用多目标框架保障服务水准
- 适合需要应对波动需求的客服、呼叫中心等场景
高效的员工班次调度对业务运营至关重要，尤其在云客服（CCaaS）行业，需兼顾运营连续性与员工需求。现有基于数学模型的方法通常将其视为单步过程，易导致效率低下和计算开销大。本文提出一种多阶段分配方法，将问题分解为日级与班次级子问题，显著减少变量数量，并支持针对性目标函数设计，从而提升效率与精度。每个子问题建模为整数规划问题（IPP），解依次传递至下一阶段。采用多目标框架，有效应对节日期间高峰需求带来的挑战，在人力有限情况下仍能维持服务水平。
原文摘要 · Abstract (English)
Effective agent shift scheduling is crucial for businesses, especially in the Contact Center as a Service (CCaaS) industry, to ensure seamless operations and fulfill employee needs. Most studies utilizing mathematical model-based solutions approach the problem as a single-step process, often resulting in inefficiencies and high computational demands. In contrast, we present a multi-phase allocation method that addresses scalability and accuracy by dividing the problem into smaller sub-problems of day and shift allocation, which significantly reduces number of computational variables and allows for targeted objective functions, ultimately enhancing both efficiency and accuracy. Each subproblem is modeled as a Integer Programming Problem (IPP), with solutions sequentially feeding into the subsequent subproblem. We then apply the proposed method, using a multi-objective framework, to address the difficulties posed by peak demand scenarios such as holiday rushes, where maintaining service levels is essential despite having limited number of employees
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