Optimized Agent Shift Scheduling Using Multi-Phase Allocation Approach

TL;DR

This paper introduces a multi-phase allocation approach for agent shift scheduling in CCaaS, improving scalability and accuracy by dividing the problem into smaller sub-problems modeled as Integer Programming Problems.

Contribution

The paper presents a novel multi-phase method that enhances efficiency and accuracy in agent shift scheduling by decomposing the problem into smaller, targeted sub-problems.

Findings

Reduces computational complexity compared to single-step models

Improves scheduling accuracy during peak demand scenarios

Enhances scalability for large-scale scheduling problems

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