This paper presents a novel methodology for constructing maximum-margin classifiers which are robust to interval-valued uncertainty in examples. The idea is to employ chance-constraints which ensure that the uncertain examples are classified correctly with high probability. The key novelty is in employing Bernstein bounding schemes to relax the resulting chance-constrained program as a convex s...

In this study, a fuzzy bi-level chance constraint programming (FBCP) model is developed for urban ecological management in Xiamen, China. FBCP has advantages balancing trade-offs between multiple decision makers and can address stochastic uncertainty ecosystem management. It also reflect the impact of different violation risk levels emission reduction measures on system benefit, service value, ...

We consider a chance constraint Prob{ξ : A(x, ξ) ∈ K} ≥ 1 − 2 (x is the decision vector, ξ is a random perturbation, K is a closed convex cone, and A(·, ·) is bilinear). While important for many applications in Optimization and Control, chance constraints typically are “computationally intractable”, which makes it necessary to look for their tractable approximations. We present these approximat...

In this paper, a novel chance-constrained programming model has been proposed for handling uncertainties in green closed loop supply chain network design. addition to locating the facilities and establishing flow between them, also determines transportation mode facilities. The objective functions are applied minimize expected value variance of total cost CO 2 released is controlled by providin...

This paper presents a stochastic model predictive control approach for nonlinear systems subject to time-invariant probabilistic uncertainties in model parameters and initial conditions. The stochastic optimal control problem entails a cost function in terms of expected values and higher moments of the states, and chance constraints that ensure probabilistic constraint satisfaction. The general...

In decision making problems where uncertainty plays a key role and decisions have to be taken prior to observing uncertainty, chance constraints are a strong modelling tool for defining safety of decisions. These constraints request that a random inequality system depending on a decision vector has to be satisfied with a high probability. The characteristics of the feasible set of such chance c...

Stochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a P-Space task. The only complete solution approach to date — scenario-based stochastic constraint programming — compiles SCSPs down into classical CSPs. This allows the reuse of classical constraint solvers to solve SCSPs, but at the cost of increased space req...