We examine the convexity and tractability of the two-sided linear chance constraint model under Gaussian uncertainty. We show that these constraints can be applied directly to model a larger class of nonlinear chance constraints as well as provide a reasonable approximation for a challenging class of quadratic chance constraints of direct interest for applications in power systems. With a view ...

A distributionally robust joint chance constraint involves a set of uncertain linear inequalities which can be violated up to a given probability threshold , over a given family of probability distributions of the uncertain parameters. A conservative approximation of a joint chance constraint, often referred to as a Bonferroni approximation, uses the union bound to approximate the joint chance ...

When dealing with real-world optimization problems, decision-makers usually face high levels of uncertainty associated partial information, unknown parameters, or complex relationships between these and the problem decision variables. In this work, we develop a novel Chance Constraint Learning (CCL) methodology focus on mixed-integer linear problems which combines ideas from chance constraint l...

Development of every society is incumbent upon energy sector’s technological and economic effectiveness. The electricity industry is a growing and needs to have a better performance to effectively cover the demand. The industry requires a balance between cost and efficiency through careful design and planning. In this paper, a two-stage stochastic programming model is presented for the design o...

We study stochastic variants of flow-based global constraints as combinatorial chance constraints. As a specific case study, we focus on the stochastic weighted alldifferent constraint. We first show that determining the consistency of this constraint is NP-hard. We then show how the combinatorial structure of the alldifferent constraint can be used to define chance-based filtering, and to comp...

Group decision-making (GDM) in an ambiguous environment has consistently become a research focus the decision science field during past decade. Existing minimum cost consensus models either control total budget deterministic context or on improving utility of makers. In this study, novel model with distributionally robust chance constraint (DRO-MCCM) is explored. First, two constraints are deve...