CALL CENTERS (CENTRES) - Faculty of Industrial Engineering and ...

algorithm (ISA), (ii) the square-root-staffing rule with service grade determined by the modifiedoffered-load
approximation, and (iii) simply staffing at the offered load itself.
Keywords: Contact centers, Call centers, Staffing, Non-stationary queues, Queues with timedependent
arrival rates, Capacity planning, Queues with abandonment, Time-varying Erlang
models
149. Feldman, Z. A. Mandelbaum, W.A. Massey and W. Whitt. Staffing of time-varying queues to
achieve time-stable performance—Unabridged version: Internet supplement to the above-named
paper. Available at .
150. Harrison, J.M. and Assaf Zeevi. A method for staffing large call centers based on stochastic fluid
models, Manufacturing & Service Operations Management, 7(1), Winter 2005, 20–36.
Abstract. We consider a call center model with m input flows and r pools of agents; the mvector
lambda of instantaneous arrival rates is allowed to be time dependent and to vary stochastically.
Seeking to optimize the trade-off between personnel costs and abandonment penalties,
we develop and illustrate a practical method for sizing the r agent pools. Using stochastic fluid
models, this method reduces the staffing problem to a multidimensional news-vendor problem,
which can be solved numerically by a combination of linear programming and Monte Carlo simulation.
Numerical examples are presented, and in all cases the pool sizes derived by means of
the proposed method are very close to optimal.
Keywords: Call center staffing, Stochastic fluid models, Call center model, Input flows, Personnel
costs, Abandonment penalties, Multidimensional news-vendor problem, Linear programming,
Monte-Carlo simulation, Capacity sizing, Fluid analysis, Queueing process, Random environment,
Stochastic programming
151. Milner, Joseph M. and Tava Lennon Olsen. Service level agreements in call centers: Perils and
prescriptions. Working paper, John M. Olin School of Business, September, 2004; Revised: July,
2005. Available at .
Abstract. A call center with both contract and non-contract customers was giving priority to
the contract customers only in off-peak hours, precisely when having priority was least important.
In this paper we investigate whether this is rational behavior on the part of the call center
and what the implications are for customers. In particular, we show that under contracts on the
percentile of delay, which are commonly used in the call-center industry, this is rational behavior,
at least under the asymptotic regime considered in the paper. We then suggest other contracts
that do not result in this type of undesirable behavior from a contract customer’s perspective.
We compare the performance of the different contracts in terms of mean, variance, and outer
percentiles of delay for both customer types using both numerical and asymptotic heavy-traffic
analyses. We argue that including terms reflecting the second moment of delay in a contract
would be beneficial to contract customers and, in a sense, fairer.
152. Ormeci, E. Lerzan and O. Zeynep Aksin. Revenue management through dynamic cross-selling
in call centers. Working paper, Koc University, August 2004; revised October 2005. Available
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