This paper proposes a new method for solving a multi-objective linear programming model with fuzzy random variables. In this model, a multi-objective linear programming problem with real variables and fuzzy random coefficients is introduced. Then, a new algorithm is developed to solve the model based on the concepts of mean value of fuzzy random variables, chance-constrained programming and pie...

The inverse spanning tree problem is to make a given spanning tree be a minimum spanning tree on a connected graph via a minimum perturbation on its edge weights. In this paper, a chance-constrained programming model is proposed to handle the inverse spanning tree problem where the edge weights are assumed to be uncertain variables. It is shown that such an uncertain minimum spanning tree can b...

In this paper, we focus on multiobjective integer programming problems with random variable coefficients in objective functions and/or constraints. For such multiobjective problems, after reformulation of them on the basis of an expectation optimization model and a variance minimization model for the chance constrained programming, incorporating fuzzy goals of the decision maker for the objecti...

This work focuses on robustness of model predictive control (MPC) with respect to satisfaction of process output constraints. A method of improving such robustness is presented. The method relies on formulating output constraints as chance constraints using the uncertainty description of the process model. The resulting on-line optimization problem is convex. The proposed approach is illustrate...

The aim of present paper is to study a constrained programming with generalized $alpha-$univex fuzzy mappings. In this paper we introduce the concepts of $alpha-$univex, $alpha-$preunivex, pseudo $alpha-$univex and $alpha-$unicave fuzzy mappings, and we discover that $alpha-$univex fuzzy mappings are more general than univex fuzzy mappings. Then, we discuss the relationships of generalized $alp...

The mixing set with a knapsack constraint arises in deterministic equivalent of chance-constrained programming problems with finite discrete distributions. We first consider the case that the chance-constrained program has equal probabilities for each scenario. We study the resulting mixing set with a cardinality constraint and propose facet-defining inequalities that subsume known explicit ine...

Dynamic game arises as a powerful paradigm for multi-robot planning, which safety constraint satisfaction is crucial. Constrained stochastic games are of particular interest, real-world robots need to operate and satisfy constraints under uncertainty. Existing methods solving handle chance using exponential penalties with hand-tuned weights. However, finding suitable penalty weight nontrivial r...