Abstract
In this paper we describe a heuristic procedure to generate solutions to a multiobjective stochastic, optimization problem for a dynamic telecommunications network. Generating Pareto optimal solutions can be difficult since the optimization problem is computationally challenging and moreover the network must be reconfigured in near real time, for example, to recover connectivity after a severe weather event. There are two main contributions of this paper. First, we show mathematically how a certain deterministic equivalent optimization problem can be solved instead of the stochastic one, thus facilitating computations. Second, we test our heuristic under a wide set of simulated conditions (e.g., atmospheric obscuration due to differing levels of cloud cover, different demand patterns) and show that it achieves near Pareto optimality in a short amount of time.
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