Optimized Agent Shift Scheduling Using Multi-Phase Allocation Approach
This addresses scheduling inefficiencies for businesses in the CCaaS industry, particularly during peak demand, but is incremental as it builds on existing integer programming approaches.
The paper tackles agent shift scheduling in contact centers by introducing a multi-phase allocation method that divides the problem into day and shift sub-problems, reducing computational variables and improving efficiency and accuracy.
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