A Method for Staffing Large Call Centers Based on Stochastic Fluid Models

Harrison, J. Michael; Zeevi, Assaf

doi:10.1287/msom.1040.0052

Cited by 139 publications

(130 citation statements)

References 20 publications

(32 reference statements)

Supporting

Mentioning

128

Contrasting

Unclassified

Order By: Relevance

“…Nevertheless, due to the relative complexity of the joint staffing-control problem, they have generally been considered separately in the literature. Recent exceptions include Armony andMaglaras (2004a, 2004b), Armony (2005), Armony and Mandelbaum (2007), Bassamboo et al (2006aBassamboo et al ( , 2006b), Harrison and Zeevi (2005), Wallace and Whitt (2005), as well as this paper.…”

Section: Introductionmentioning

confidence: 88%

Service-Level Differentiation in Call Centers with Fully Flexible Servers

2008

View full text Add to dashboard Cite

W e study large-scale service systems with multiple customer classes and many statistically identical servers.The following question is addressed: How many servers are required (staffing) and how does one match them with customers (control) to minimize staffing cost, subject to class-level quality-of-service constraints? We tackle this question by characterizing scheduling and staffing schemes that are asymptotically optimal in the limit, as system load grows to infinity. The asymptotic regimes considered are consistent with the efficiencydriven (ED), quality-driven (QD), and quality-and-efficiency-driven (QED) regimes, first introduced in the context of a single-class service system.Our main findings are as follows: (a) Decoupling of staffing and control, namely, (i) staffing disregards the multiclass nature of the system and is analogous to the staffing of a single-class system with the same aggregate demand and a single global quality-of-service constraint, and (ii) class-level service differentiation is obtained by using a simple idle-server-based threshold-priority (ITP) control (with state-independent thresholds); and (b) robustness of the staffing and control rules: our proposed single-class staffing (SCS) rule and ITP control are approximately optimal under various problem formulations and model assumptions. Particularly, although our solution is shown to be asymptotically optimal for large systems, we numerically demonstrate that it performs well also for relatively small systems.

show abstract

Section: Introductionmentioning

confidence: 88%

Service-Level Differentiation in Call Centers with Fully Flexible Servers

2008

View full text Add to dashboard Cite

show abstract

“…Therefore, we assume that µ ij is set to be constant for all planning periods in this paper. We use the abandonment model discussed by Harrison and Zeevi (2005), which is considered as a standard model in call center modeling. In this model, we assume that there is an exponential distributed random variable τ associated with each customer class i with mean 1/θ i .…”

Section: Data-driven Approach With Risk Aversionmentioning

confidence: 99%

“…Customer abandonment also needs to be taken into account. Harrison and Zeevi (2005) propose a staffing method for multi-class/multi-pool call centers with uncertain arrival rates with a known probabilistic structure of arrival rates and customer abandonment. Bassamboo and Zeevi (2007) take a data-driven approach using historical call arrival data to approximate the distribution of the arrival rate process for the same call center model.…”

mentioning

confidence: 99%

Robust and data-driven approaches to call centers

Bertsimas

Doan

2010

European Journal of Operational Research

View full text Add to dashboard Cite

We propose both robust and data-driven approaches to a fluid model of call centers that incorporates random arrival rates with abandonment to determine staff levels and dynamic routing policies.Resulting models are tested with real data obtained from the call center of a US bank. Computational results show that the robust fluid model is significantly more tractable as compared to the data-driven one and producing better solutions to call centers in most experiments.

show abstract

“…In contrast, Van Mieghem (1995) proved asymptotic optimality of a simple generalized cµ rule for a V-design with waiting costs that are convex increasing. Harrison and Zeevi (2004) studied staffing large call centers using stochastic fluid models. Armony andMaglaras (2004a, 2004b) considered multi-class, multi-server call centers with a call-back option, and proposed asymptotically optimal routing and staffing policies.…”

Section: Literature Reviewmentioning

confidence: 99%

Pooling strategies for call center agent cross-training

2009

View full text Add to dashboard Cite

We consider call center environments where agents serve distinct customer types, and investigate the efficiency benefits achievable via cross-training. We do this by first considering specialized agents grouped into N departments according to the customer type they serve.Then we examine cross-training policies that pool a set of departments K (selecting k = |K| of N ) into a single larger department in which every agent serves all of the pooled call types. For the pooled department, we analyze both First-Come-First-Served (FCFS) and Non-Preemptive Priority (NPP) service disciplines. By comparing the resulting queueing models via standard queueing approximations and numerical analysis, we characterize the impact of system parameters, such as department sizes, arrival rates and service times, on the decision of what departments to pool.

show abstract

A Method for Staffing Large Call Centers Based on Stochastic Fluid Models

Cited by 139 publications

References 20 publications

Service-Level Differentiation in Call Centers with Fully Flexible Servers

Service-Level Differentiation in Call Centers with Fully Flexible Servers

Robust and data-driven approaches to call centers

Pooling strategies for call center agent cross-training

Contact Info

Product

Resources

About