Optimized Agent Shift Scheduling Using Multi-Phase Allocation Approach
Sanalkumar K, Koushik Dey, Swati Meena
TL;DR
This paper tackles CCaaS agent shift scheduling under peak-demand and limited staffing by proposing a multi-phase allocation framework that splits the problem into agent day allocation and agent shift allocation, each formulated as Integer Programming Problems. A peak-season KL-divergence penalty is introduced to promote balanced day distributions when staffing is constrained, and Erlang-C-based demand is used to derive target staffing levels. Empirical results demonstrate substantial reductions in decision variables and scheduling deviations compared to a single-phase baseline, with a notable improvement in day distribution and service level maintenance. The approach offers scalable, accurate scheduling for real-world CCaaS environments and outlines avenues for incorporating richer constraints and break optimization in future work.
Abstract
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