Paper Info
Title
Using a genetic algorithm as a decision support tool for the deployment of Fiber Optic Networks
Abstract
Fiber optics is a relatively new technology, one which has not yet been extensively used, because of its high cost. In order to evaluate the viability of such a costly investment, techno-economic models are employed. These models evaluate the investment from both technical (e.g., optimal network design) and economical (e.g., profitability) perspectives. However, an area that has not received much attention is the deployment plans of a given fiber optic investment. Existing works usually compare manually predefined deployment plans that are considered profitable, and then apply techno-economic analysis. While this indeed offers valuable information, it does not guarantee that the examined plans are the optimal ones. This should be considered as a major disadvantage, because there could be other deployment plans that could offer significantly higher profit. This paper offers a first attempt at looking for the optimal deployment plan of fiber optics, based on profit. Our method can be considered as a framework that wraps around existing techno-economic models. We employ a Genetic Algorithm (GA), which creates a population of deployment plans. These plans can then be evaluated through the usual techno-economic approach. The GA then evolves the population of these plans and at the end of the process acts as a decision support tool that advises on the optimal deployment plan, without the need for any human interference in the decision-making process. For comparison purposes, we compare the GA's results with results under other profitable plans. Results show that the introduction of the use of the GA is very advantageous and leads to a significant increase in profit.
Year
DOI
Venue
2012
10.1109/CEC.2012.6252864
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
decision making,economics,genetic algorithms,optical fibre networks,decision making,decision support tool,fiber optic networks,genetic algorithm,human interference,techno-economic models
Population,Mathematical optimization,Software deployment,Network planning and design,Computer science,Decision support system,Operations research,Profitability index,Interference (wave propagation),Management science,Genetic algorithm,Disadvantage
Conference
ISBN
Citations 
PageRank 
978-1-4673-1508-1
0
0.34
References 
Authors
4
4
Name
Order
Citations
PageRank
Michael Kampouridis19416.60
Glover, T.200.34
Ali Rais Shaghaghi300.34
Edward P. K. Tsang489987.77