| A methodology for network design under demand uncertainty is proposed in this dissertation. The uncertainty is caused by the dynamic nature of the IP-based traffic which is expected to be transported directly over the optical layer in the future. Thus, there is a need to incorporate the uncertainty into a design model explicitly. We assume that each demand can be represented as a random variable, and then develop an optimization model to minimize the cost of routing and bandwidth provisioning. The optimization problem is formulated as a nonlinear Multicommodity Flow problem using Chance-Constrained Programming to capture both the demand variability and levels of uncertainty guarantee. Numerical work is presented based on a heuristic solution approach using a linear approximation to transform the nonlinear problem to a simpler linear programming problem. In addition, the impact of the uncertainty on a two-layer network is investigated. This will determine how the Chance-Constrained Programming based scheme can be practically implemented. Finally, implementation guidelines for developing an updating process are provided. |
