We present a distributed iterative improvement algorithm for solving coarse-grained distributed constraint satisfaction problems (DisCSPs). Our algorithm is inspired by the Distributed Breakout for coarse-grained DisCSPs where we introduce a constraint weight decay and a constraint weight learning mechanism in order to escape local optima. We also introduce some randomisation in order to give t...

This paper investigates the computational aspects of distributionally robust chance constrained optimization problems. In contrast to previous research that mainly focused on the linear case (with a few exceptions discussed in detail below), we consider the case where the constraints can be non-linear to the decision variable, and in particular to the uncertain parameters. This formulation is o...

Joint chance constrained problems give rise to many algorithmic challenges. Even in the convex case, i.e., when an appropriate transformation of the probabilistic constraint is a convex function, its cutting-plane linearization is just an approximation, produced by an oracle providing subgradient and function values that can only be evaluated inexactly. As a result, the cutting-plane model may ...

Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two following properties with high probability: (i) its probability of type I error is below a pre-specifie...

Uncertainty issues associated with a multi-site, multi-product supply chain planning problem has been analyzed in this paper using the chance constraint programming approach. In literature, such problems have been addressed using the two stage stochastic programming approach. While this approach has merits in terms of decomposition, computational complexity even for small size planning problem ...

This study covers the output-feedback model predictive control (MPC) of nonlinear systems subjected to stochastic disturbances and state chance constraints. The optimal problem is solved in a dynamic programming fashion, performed with extended Kalman filter. information summarized as Gaussian belief model. Thus, Bellman equation transformed into deterministic using this resulting constrained p...

Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two following properties with high probability: (i), its probability of type I error is below a pre-specifi...

This paper proposes a chance-constrained multi-objective goal programming model for supplier selection problem with uncertain factors. Considering uncertain factors of demand, capacity and lead time and several objectives, the proposed approach provides chance constraints leading to a order allocation decision-making result. The decision keeps the confidence and risk of constraints to a certain...

We consider a two player bimatrix game where the entries of the payoff matrices are random variables. We formulate this problem as a chance-constrained game by considering that the payoff of each player is defined using a chance constraint. We consider the case where the entries of the payoff matrices are independent normal/Cauchy random variables. We show a one-to-one correspondence between a ...