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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Cunbin Li Kecheng Wang |
| Copyright Year | 2007 |
| Description | Author affiliation: North China Electr. Power Univ., Beijing (Cunbin Li; Kecheng Wang) |
| Abstract | Risk management project is an important aspect of general project risk element transmission theory. Traditional network planning technology encountered great obstacles in project risk management issues, and often unable to accurately forecast the risk, resulting great loss of costs. To address the cost-time optimization problem considering the risk elements, this paper established a network planning risk element model, which divides risk elements into discrete model and continuous model to be discussed separately. In the discrete model costs and risk element matrix is introduced to get the corresponding programming model; In Continuous model the idea of machine learning model is used to minimum the desired risk. Based on this model, by using genetic algorithm's efficient and rapid global search capability, this paper improves the genetic algorithm developed by Feng and others, increases the risk elements and eventually gets the cost-time curve considering risk elements. This effectively solves the network planning cost optimization problem. |
| Starting Page | 170 |
| Ending Page | 174 |
| File Size | 259496 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769528755 |
| DOI | 10.1109/ICNC.2007.739 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-24 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Costs Project management Machine learning Technology forecasting Probability distribution Risk management Mathematical model Technology planning Energy management Genetic algorithms |
| Content Type | Text |
| Resource Type | Article |
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