Service centers can be viewed as facilities wherein service calls from customers relating to various service families cascade into work packages involving a set of service tasks. A service family is defined as a set of service type variants that have similar or common service tasks and hence may use a common set of resources (called the resource platform) over a given time horizon. In this paper, we present a methodology for determining cost-effective and robust resource platform configuration(s) for a given set of service families offered by a service center. The robust resource platform would be able to handle demand fluctuations from various service types within reasonable limits over a given planning horizon. Several successful studies have been reported in the manufacturing domain on successful application of product platforms to generate customizable product variants with cost advantages. In this paper, we extend the product platform concept to the service domain. The critical parts of the proposed Service Resource Platforming System (SRPS) methodology are: (1) generate rough cut resource selection using dynamic programming and create a resource schedule using linear programming; (2) generate final resource selection using uncertainty linear programming model proposed by Ben-Tal et al. (2003); and (3) construct resource platform using resource sub-sequence clustering concept. The objective for the rough cut and final resource selections is the maximization of the difference between the benefits and costs associated with in-house service processing and outsourcing. The proposed SRPS methodology is applied to an industry motivated problem.

1.
Gans
N.
,
Koole
G.
and
Mandelbaum
A.
,
2003
, “
Telephone Call Centers: Tutorial, Review, and Research Prospects
,”
Manufacturing & Service Operations Management
,
5
, pp.
79
141
.
2.
Wu, Z. and Weng, M. X., 2005, “Dynamic Due Date Setting and Shop Scheduling for Make-to-Order Companies,” IIE IERC Annual Conference, Atlanta, GA.
3.
Satyam, K. and Krishnamurthy, A., 2005, “Analytical Models for Multi-Product Systems with Shared Resources,” IIE-IERC Annual Conference.
4.
Gupta
S.
and
Krishnan
V.
,
1999
, “
Integrated Component and Supplier Selection for a Product Family
,”
Production & Operations Management
, Summer 1999,
8
(
2
), pp.
163
181
.
5.
Steckley, S. G., and Henderson, S. G., 2003, “A kernel approach to estimating the density of a conditional expectation,” In Proceedings of the 2003 Winter Simulation Conference, ed. S. Chick, P. Sa´nchez, D. Ferrin, and D. Morrice, pp. 383–391.
6.
Borst, S.C., Mandelbaum, A. and Reiman, M.I., 2000, “Dimensioning large call centers,” Technion. (http://ftp.cwi.nl/CWIreports/PNA/PNA-R0015.pdf)
7.
Jennings
O. B.
,
Mandelbaum
A.
,
Massey
W. A.
and
Whitt
W.
,
1996
, “
Server staffing to meet time varying demand
,”
Management Science
,
42
, pp.
1383
1394
.
8.
Lariviere
M.
and
Van Mieghem
J. A.
,
2004
, “
Strategically Seeking Service: How Competition Can Generate Poisson Arrivals
,”
Manufacturing & Service Operations Management
,
6
(
1
), pp.
23
40
.
9.
Lopez-Alvarado, P.A. and Centeno, G., 2005, “Integrated Scheduling and Information Support System for Transit Maintenance Departments” IIE - IERC Annual Conference, Atlanta, GA.
10.
Cohen, Y., Sadeh, A. and Zwikael, O., 2005, “An Efficient Technique for Finding the Shortest Non-Delay Schedule for a Resource-Constrained Project,” IIE-IERC Annual Conference, Atlanta, GA.
11.
Simpson
T. W.
,
2004
, “
Product Platform Design and Customization: Status and Promise
,”
Artificial Intelligence for Engineering Design and Manufacturing
,
18
(
1
), pp.
3
20
.
12.
Rai
R.
,
Allada
V.
,
2003
, “
Modular product family design: agent-based Pareto-optimization and quality loss function-based post-optimal analysis
,”
International Journal of Production Research
,
41
(
17
), pp.
4075
4098
.
13.
Kumar, R., and Allada, V., 2005, “Scalable Platforms using Ant Colony Optimization” to appear in Journal of Intelligent Manufacturing.
14.
Baker
K. R.
,
Magazine
M. J.
and
Nuttle
H. L. W.
,
1986
, “
The effect of commonality on safety stock in a simple inventory model
,”
Management Science
,
32
(
8
), pp.
982
988
.
15.
Green
P. E.
and
Krieger
A. M.
,
1985
, “
Models and heuristics for product line selection
,”
Marketing Science
,
4
(
1
), pp.
1
19
.
16.
Lancaster
K.
,
1990
, “
The economics of product variety
,”
Marketing Science
,
9
(
3
), pp.
189
206
.
17.
Williams C. B., Allen, J. K., Rosen, D. W. and Mistree F., 2004, “Designing Platforms for Customizable Products in Markets with Non-Uniform Demand,” 16th ASME Design Theory and Methodology Conference, Salt Lake, DETC2004-57469.
18.
Dai, Z. and Scott, M. J., 2004, “Effective Product Family Design using Preference Aggregation,” ASME DETC Conference and CIEC, DETC2004-57419.
19.
Park, B.J., Ghosh, S. and Murthy, N., 2000, “A Framework for Integrating Product Platform Development with Global Supply Chain Configuration,” 31st Annual Meeting of the Decision Sciences Institute, 3, pp. 1138–1140.
20.
Irani
S. A.
and
Huang
H.
,
2000
, “
Custom design of facility layouts for multi-product facilities using layout modules
,”
IEEE Transactions on Robotics and Automation
,
16
(
3
), pp.
259
267
.
21.
Simpson, T. and Souza, B., 2002, “Assessing Variable Levels of Platform Commonality Within a Product Family Using a Multi-objective Genetic Algorithm,” 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, AIAA 2002-5427.
22.
Martin
M.
and
Ishii
K.
,
2002
, “
Design for Variety: Developing Standardized and Modularized Product Platform Architecture
,”
Research in Engineering Design
,
1
, pp.
213
235
.
23.
Logendran R., McDonell, B., and Smucker, B., 2005, “Unrelated Parallel Machine scheduling with Sequence Dependent Setups,” IIE- IERC Annual Conference, Atlanta, GA.
24.
Kunadilok, J. and Kurz, M. E., 2005, “Scheduling flexible mixed shops with multiple processing restrictions by mixed integer programming,” IIE- IERC Annual Conference, Atlanta, GA.
25.
Ben-Tal, A., Goryashko, A, Guslitzer, E. and Nemirovski, A., 2003, “Adjustable Robust Solutions of Uncertain Linear Programs,” Technical Report, Minerva Optimization Center, Technion.
26.
Angus
I.
,
2001
, “
An introduction to Erlang B and C
,”
Telemanagement
, No.
187
, pp.
6
9
.
27.
Gans
N.
and
Zhou
Y.
,
2003
, “
A call-routing problem with Service-level constraints
,”
Operations Research
,
51
(
2
), pp.
255
271
.
28.
Brown
L.
,
Gans
N.
,
Mandelbaum
A.
,
Sakov
A.
,
Shen
H.
,
Zeltyn
S.
and
Zhao
L.
,
2002
, “
Statistical analysis of a telephone call center: A queueing-science perspective
,”
Journal of the American Statistical Association
,
100
(
469
), pp.
36
50
.
29.
Henderson
H. G.
and
Glynn
P. W.
,
2001
, “
Computing Densities for Markov Chains Via Simulation
Mathematics of Operations Research
,
26
(
2
), pp.
375
400
.
30.
Jelenkovic, P., Mandelbaum, I. and Momcilovic, P., June 2003, “Heavy Traffic Limits for Queues with Many Deterministic Servers,” Research sponsored by Israel Science Foundation.
31.
Mandelbaum, A., Massey, W.A., Reiman M. and Rider, B., 1999, “Time Varying Multiserver Queues with Abandonment and Retrials,” Teletraffic Engineering in a Competitive World, Editors P. Key and D. Smith, Elsevier, pp. 355–364.
32.
Sze
D. Y.
,
1984
, “
A queuing model for telephone operator staffing
,”
Operations Research
,
32
, pp.
229
249
.
33.
Huang, H. and Irani, S.A., 1999, “Design of Facility Layouts using Layout Modules: A Numerical Clustering Approach,” Proceedings of the 8th Annual Industrial Engineering Research conference, May 23–26 1999.
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