# Topics in Workforce Management in a Contact Center Context

**Time: **
Fri 2019-09-27 14.00

**Location: **
F3, Lindstedtsvägen 26, Stockholm (English)

**Subject area: **
Applied and Computational Mathematics

**Doctoral student: **
Göran Svensson
, Optimeringslära och systemteori

**Opponent: **
Professor Tolga Tezcan,

**Supervisor: **
Associate Professor Per Enqvist, Optimeringslära och systemteori

## Abstract

This thesis is written as a monograph covering topics in operations research and focusing on workforce

management in a contact center environment.

This text is the result of a cooperative project between the Royal Institute of Technology (KTH) and

Teleopti WFM.

The main objective is to transform everyday problems faced at Teleopti into a mathematical

modeling framework.

The modeling aspect plays a prominent role and therefore, a large portion of this thesis deals with

the modeling aspects of contact center management.

The majority of the models are proposed in terms of Markov queuing networks.

The text is divided into five chapters.

The first chapter covers the introduction and provides a short background of

the basics of contact centers and workforce management.

It also briefly mentions the necessary mathematical tools.

In Chapter 2, a multiclass and multiserver queuing network with a common budget constraint is introduced.

The multiobjective optimization problem of minimizing server costs while delivering a high quality of service

is solved using the marginal allocation algorithm.

The quality of service measures used to quantify customer satisfaction is the conditional value-at-risk

measure and the fraction of abandoning customers.

It is proved that the conditional value-at-risk measure is integer convex in terms of the number of servers

when the customer waiting time is taken as the loss function.

In Chapter 3, the contact center interagent fairness is considered.

The importance of agent happiness in face of attrition is briefly discussed.

To include the interagent fairness into the modeling procedure a multiclass and multiserver queuing

network is introduced.

The servers are grouped into pools of exchangeable agents serving a subset of the customer classes.

The interagent fairness measure can be introduced either as part of the objective function or as part

of the optimization constraints.

Robustness and multiperiod solutions are also considered.

In Chapter 4, a limited state dependent server sharing system is considered in the context of

a chat based communication system.

The proposed model is an extension of the Markov queuing model applied to telephone based

communication systems, where the agents may serve several customers concurrently.

The service intensity provided depend on how many concurrent customers an agent serves which

increases the complexity of the model.

It is shown how agents of similar performance can be categorised together into groups and thus be handled separately.

Several results pertaining to solving such a system are introduced and exemplified.

In Chapter 5, the estimation of the parameters of the model presented in Chapter 4 is considered.

This process is strongly data dependent, i.e., data driven, and a data classification system is proposed

to the data available for the estimation.

The chapter then proceeds to

investigate frequentist and Bayesian strategies of parameter

estimation under conditions of high and low resolution data

Furthermore, the model in Chapter 4 is evaluated in terms of a real chat center data set.