Probabilistic Constrained Optimization on Flow Networks

TL;DR

This paper develops methods to compute the probability that uncertain boundary data in flow networks satisfy constraints, introducing two approaches and deriving optimality conditions with numerical comparisons to Monte Carlo methods.

Contribution

It introduces two novel methods for probabilistic feasibility analysis in flow networks with uncertainty and derives optimality conditions for these models.

Findings

The spheric radial decomposition and kernel density estimation effectively compute feasibility probabilities.

Derived necessary optimality conditions for both stationary and dynamic flow models.

Numerical results demonstrate the methods' effectiveness compared to Monte Carlo simulations.

Abstract

Uncertainty often plays an important role in dynamic flow problems. In this paper, we consider both, a stationary and a dynamic flow model with uncertain boundary data on networks. We introduce two different ways how to compute the probability for random boundary data to be feasible, discussing their advantages and disadvantages. In this context, feasible means, that the flow corresponding to the random boundary data meets some box constraints at the network junctions. The first method is the spheric radial decomposition and the second method is a kernel density estimation. In both settings, we consider certain optimization problems and we compute derivatives of the probabilistic constraint using the kernel density estimator. Moreover, we derive necessary optimality conditions for the stationary and the dynamic case. Throughout the paper, we use numerical examples to illustrate our results by comparing them with a classical Monte Carlo approach to compute the desired probability.