ENBIS: European Network for Business and Industrial Statistics
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ENBIS8 in Athens
21 – 25 September 2008 Abstract submission: 14 March – 11 August 2008The following abstracts have been accepted for this event:

Poisson approximation for Consecutive Covering Arrays
Authors: F.S. Milienos, M.V. Koutras,A.P. Godbole
Affiliation: Department of Statistics and Actuarial Science,University of Piraeus, Greece; Department of Mathematics, East Tennessee State University, USA
Primary area of focus / application:
Keywords: tcovering arrays, consecutive covering arrays, Markov chains, random matrices, factorial designs
t×n submatrix contains amongst its columns each one of the qt different words of length
t that can be produced by the q letters (see e.g. Colbourn (2004), Godbole et al. (1996)
and Dalal and Mallows (1998)). In the present article we study a tcovering problem
where, instead of looking at all possible t × n submatrices, we consider only submatrices
of dimension t×n with its rows being consecutive rows of the original k ×n array. In the
present work, exploiting the celebrated SteinChen method (Barbour et al. (1992)), we
establish a Poisson approximation result for an enumerating random variable, that counts
the number of submatrices consisting of consecutive rows of the original k × n array, in
which, at least one word is missing. Finally, a potential application in the field of factorial
designs is also included.
Keywords. tcovering arrays, consecutive covering arrays, Markov chains, random matrices,
factorial designs
References
[1] Barbour, A.D., Holst, L. and Janson, S. (1992). Poisson Aproximation. Oxford Univ. Press.
[2] Colbourn C.J. (2004). Combinatorial aspects of covering arrays, Le Matematiche (Catania),
58, 121167.
[3] Dalal S.R. and Mallows C.L. (1998). Factorcovering designs for testing software. Technometrics,
40, 234243.
[4] Godbole A.P., Skipper D.E. and R.A. Sunley (1996). tcovering arrays: upper bounds and
Poisson approximations. Combinatorics, Probability and Computing, 5, 105118. 
Design Schemes for the Xbar Control Chart
Authors: Marit Schoonhoven Muhammed Riaz Ronald J.M.M. Does
Affiliation: IBIS UvA
Primary area of focus / application:
Submitted at 25Jul2008 13:19 by Marit Schoonhoven
Accepted

QED Queues: Quality and EfficiencyDriven Call Centers
Authors: Avishai (Avi) Mandelbaum
Affiliation: Industrial Engineering & Management, Technion; http://ie.technion.ac.il/serveng
Primary area of focus / application:
Submitted at 10Aug2008 12:26 by Avi Mandelbaum
Accepted
empirical findings that motivate or are motivated by (or both) interesting research questions. These
findings give rise to features that are prerequisites for useful service models, for example customers’
(im)patience, timevarying demand, heterogeneity of customers and servers, overdispersion in Poisson
arrivals, generallydistributed (as opposed to exponential) service and patiencedurations, and
more. Empirical analysis also enables validation of existing models and protocols, either supporting
or refuting their relevance and robustness.
The mathematical framework for my models is asymptotic queueing theory, where limits
are taken as the number of servers increases indefinitely, in a way that maintains a delicate balance
against the offeredload. Asymptotic analysis reveals an operational regime that achieves, under
already moderate scale, remarkably high levels of both service quality and efficiency. This is the
QED Regime, discovered by Erlang and characterized by Halfin & Whitt. (QED = Quality and
EfficiencyDriven).
My main datasource is a unique repository of callcenters data, designed and maintained at
the Technion’s SEE Laboratory. (SEE = Service Enterprise Engineering). The data is unique in
that it is transactionbased: it details the individual operational history of all the calls handled by
the participating call centers. (For example, one source of data is a network of 4 call centers of a
U.S. bank, spanning 2.5 years and covering about 1000 agents; there are 218,047,488 telephone calls
overall, out of which 41,646,142 where served by agents, while the rest were handled by answering
machines.) To support data analysis, a universal datastructure and a friendly interface have been
developed, under the logo DataMOCCA = Data MOdels for Call Centers Analysis. (I shall have
with me DataMOCCA DVD’s for academic distribution.)
Background Reading
1. Gans, N., Koole, G., Mandelbaum, A. “Telephone Call Centers: Tutorial, Review and Research
Prospects.” Invited review paper by Manufacturing and Service Operations Management
(M&SOM), 5 (2), 79141, 2003.
http://iew3.technion.ac.il/serveng/References/GansKooleMandelbaumCCReview.pdf
2. Brown, L., Gans, N., Mandelbaum, A., Sakov, A., Zeltyn, S., Zhao, L. and Haipeng, S.
“Statistical Analysis of a Telephone Call Center : A QueueingScience Perspective.” Journal
of the American Statistical Association (JASA), 100, 3650, 2005.
http://iew3.technion.ac.il/serveng/References/JASA callcenter.pdf 
A New Response: An Approach to Handling Systematically Missing Data in a Designed Experiment
Authors: Ewan Polwart, Owen Lozman, Richard Williams
Affiliation: FUJIFILM Imaging Colorants
Primary area of focus / application:

Design of Six Sigma
Authors: Jonathan SmythRenshaw
Primary area of focus / application:
Keywords: Six Sigma
Submitted at 8Sep2008 09:43 by Jonathan SmythRenshaw
Accepted