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

improvement over the previous manual procedure.
Keywords: Operator shift assignment, New Brunswick Telephone Company, Specialized shift
assignment heuristic, Spreadsheet, Management, Employees, Optimisation
56. Fischer, M.J., D.A. Garbin and A. Gharakhanian. Performance modeling of distributed automatic
call distribution systems, Telecommunication Systems—Modeling, Analysis, Design and
Management, 9 (2), 1998, 133–152.
Abstract. The number of businesses using automatic call distribution (ACD) systems has
grown significantly in the last five years. The industry shows all the signs of continued or
greater growth in the foreseeable future. While ACD systems have proliferated they have also
evolved from fundamentally local to distributed systems. An ACD manager can no longer optimize
his traffic by using inputs from a simple set of queueing tables. The most common system
is now a distributed network where subsystems interact with each other and cannot be analyzed
in isolation. This paper examines the strengths and weaknesses of queueing models that have
been used historically with ACD systems and develops modifications to these models (including
agent wrap-up times) that are combined with queueing network theories to construct an original
ACD network performance algorithm to work with distributed systems.
Keywords: Automatic call distribution systems, Businesses, ACD, Distributed network, Agent
wrap-up times, Queueing network theories, Network performance algorithm, Traffic optimization
57. Kolesar, Peter J. and Linda V. Green. Insights on service system design from a normal approximation
to Erlang’s delay formula, Production and Operations Management, 7 (3), 1998, 282–293.
Abstract. We show how a simple normal approximation to Erlang’s delay formula can be used
to analyze capacity and staffing problems in service systems that can be modeled as M/M/s
queues. The numbers of servers, s, needed in an M/M/s queueing system to assure a probability
√
of delay of, at most, p can be well approximated by s ≈ ρ + z1−p ρ, where z1−p is the (1 − p)th
percentile of the standard normal distribution and ρ, the presented load on the system, is the
ratio of λ, the customer arrival rate, to µ, the service rate. We examine the accuracy of this
approximation over a set of parameters typical of service operations ranging from police patrol,
through telemarketing to automatic teller machines, and we demonstrate that it tends to slightly
underestimate the number of servers actually needed to hit the delay probability target—adding
one server to the number suggested by the above formula typically gives the exact result. More
importantly, the structure of the approximation promotes operational insight by explicitly linking
the number of servers with server utilization and the customer service level. Using a scenario
based on an actual teleservicing operation, we show how operations managers and designers can
quickly obtain insights about the trade-offs between system size, system utilization and customer
service. We argue that this little-used approach deserves a prominent role in the operations analyst’s
and operations manager’s tool bags.
Keywords: Erlang’s delay formula, M/M/s queue, Service system design, Normal approximation,
Staffing levels
58. Mandelbaum, A. and S. Zeltyn. Estimating characteristics of queueing networks using transac-
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