Details
| Original language | English |
|---|---|
| Pages (from-to) | 425-435 |
| Number of pages | 11 |
| Journal | Engineering optimization |
| Volume | 37 |
| Issue number | 4 |
| Publication status | Published - 1 Jun 2005 |
Abstract
The liberalization of telecommunication markets allows the choice of call provider for each single connection. This leads to the question of which provider has to be chosen to minimize the charge volume for all the calls over an accounting period. In contrast to the simple derivation of routing policies found in existing least cost routing (LCR) tables, the incremental and global quantity discounts granted by providers are taken into account in the optimization approach developed in this paper. The dynamic, non-convex, non-linear optimization problem arising from the minimization of the charge volume is solved using the evolutionary optimization method of genetic algorithms. The mathematical formulation of the problem, the genetic algorithm used, the limitation of the search space, and the quantity discount regulation developed are presented. The optimization results obtained by applying the concept to the call data of the University of Hanover are analysed and discussed. The paper concludes with an outlook for the implementation of LCR policies.
Keywords
- Genetic algorithms, Least cost routing, Simulation, Telecommunications
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Control and Optimization
- Decision Sciences(all)
- Management Science and Operations Research
- Engineering(all)
- Industrial and Manufacturing Engineering
- Mathematics(all)
- Applied Mathematics
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In: Engineering optimization, Vol. 37, No. 4, 01.06.2005, p. 425-435.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Charge volume minimization in liberalized telecommunication markets
AU - Niemann, Frank
AU - Schulze, Cord
AU - Jobmann, Klaus
PY - 2005/6/1
Y1 - 2005/6/1
N2 - The liberalization of telecommunication markets allows the choice of call provider for each single connection. This leads to the question of which provider has to be chosen to minimize the charge volume for all the calls over an accounting period. In contrast to the simple derivation of routing policies found in existing least cost routing (LCR) tables, the incremental and global quantity discounts granted by providers are taken into account in the optimization approach developed in this paper. The dynamic, non-convex, non-linear optimization problem arising from the minimization of the charge volume is solved using the evolutionary optimization method of genetic algorithms. The mathematical formulation of the problem, the genetic algorithm used, the limitation of the search space, and the quantity discount regulation developed are presented. The optimization results obtained by applying the concept to the call data of the University of Hanover are analysed and discussed. The paper concludes with an outlook for the implementation of LCR policies.
AB - The liberalization of telecommunication markets allows the choice of call provider for each single connection. This leads to the question of which provider has to be chosen to minimize the charge volume for all the calls over an accounting period. In contrast to the simple derivation of routing policies found in existing least cost routing (LCR) tables, the incremental and global quantity discounts granted by providers are taken into account in the optimization approach developed in this paper. The dynamic, non-convex, non-linear optimization problem arising from the minimization of the charge volume is solved using the evolutionary optimization method of genetic algorithms. The mathematical formulation of the problem, the genetic algorithm used, the limitation of the search space, and the quantity discount regulation developed are presented. The optimization results obtained by applying the concept to the call data of the University of Hanover are analysed and discussed. The paper concludes with an outlook for the implementation of LCR policies.
KW - Genetic algorithms
KW - Least cost routing
KW - Simulation
KW - Telecommunications
UR - http://www.scopus.com/inward/record.url?scp=20444434388&partnerID=8YFLogxK
U2 - 10.1080/03052150500035534
DO - 10.1080/03052150500035534
M3 - Article
AN - SCOPUS:20444434388
VL - 37
SP - 425
EP - 435
JO - Engineering optimization
JF - Engineering optimization
SN - 0305-215X
IS - 4
ER -