This paper is part of the ONBOARD1 project, whose goal is to investigate the incorporation of probabilistic information about the levels of uncertainty in ATFM. The efficiency of ATFM optimizations in preventing local demand-capacity imbalances is reliant on accurate predictions of future capacity states. However these predictions are inherently uncertain due to factors such as weather effects ...

Many engineered systems, such as energy and transportation infrastructures, are networks governed by non-linear physical laws. A primary challenge for operators of these networks is to achieve optimal utilization while maintaining safety and feasibility, especially in the face of uncertainty regarding the system model. To address this problem, we formulate a Chance Constrained Optimal Physical ...

Abstract Renewable energy has unpredictable intermittency and variability, increasing the complexity of grid dispatch. Fuzziness theory is introduced under uncertainty large-scale intermittent power supply. characterized by fuzziness parameters. Meanwhile, conventional deterministic unit commitment model transformed into a chance-constraint with The explicit equivalence classes chance constrain...

This paper develops a solution method for Chance Constrained Programming problem, where the random variable in the right hand side of the chance constraints follows different fuzzy distributions. The methodology is verified through numerical examples.

In this paper, we study the link between a Chance-Constrained optimization Problem (CCP) and its sample counterpart (SP). SP has a finite number, say N , of sampled constraints. Further, some of these sampled constraints, say k, are discarded, and the final solution is indicated by x∗N,k. Extending previous results on the feasibility of sample convex optimization programs, we establish the feas...

We consider the convexity of chance constraints with random righthand side. While this issue is well understood (thanks to Prékopa’s Theorem) if the mapping operating on the decision vector is componentwise concave, things become more delicate when relaxing the concavity property. In an earlier paper, the significantly weaker r-concavity concept could be exploited, in order to derive eventual c...

A joint chance constrained optimization problem involves multiple uncertain constraints, i.e., constraints with stochastic parameters, that are jointly required to be satisfied with probability exceeding a prespecified threshold. In a distributionally robust joint chance constrained optimization problem (DRCCP), the joint chance constraint is required to hold for all probability distributions o...

It is challenging to generate optimal trajectories for nonlinear dynamic systems under external disturbances. In this brief, we present a novel approach planning safe of the chance-constrained trajectory optimization problems with nonconvex constraints. First, chance constraints are handled by deterministic ones which show its availability. We derive an iterative convex method solve control pro...

This paper deals with chance constraints based reliability stochastic optimization problem in the series system. This problem can be formulated as a nonlinear integer programming problem of maximizing the overall system reliability under chance constraints due to resources. The assumption of traditional reliability optimization problem is that the reliability of a component is known as a fixed ...

We consider a container routing problem with stochastic time variables in a sea-rail intermodal transportation system. The problem is formulated as a binary integer chance-constrained programming model including stochastic travel times and stochastic transfer time, with the objective of minimising the expected total cost. Two chance constraints are proposed to ensure that the container service ...