Bounds for probabilistic integer programming problems

Bounds for probabilistic integer programming problems

We consider stochastic integer programming problems with probabilistic constraints. The concept of a p-efficient point of a probability distribution is used to derive various equivalent problem formulations. Next we introduce new methods for constructing lower and upper bounds for probabilistically constrained integer programs. We also show how limited information about the distribution can be ...

Lower bounds for integer programming problems

A new approach for solving neutrosophic integer programming problems

Linear programming is one of the most important usages of operation research methods in real life, that includes of one objective function and one or several constraints which can be in the form of equality and inequality. Most of the problems in the real world are include of inconsistent and astute uncertainty, because of this reason we can’t obtain the optimal solution easily. In this paper, ...

Valid Linear Programming Bounds for Exact Mixed-Integer Programming

Probabilistic Programming for Planning Problems

Probabilistic programing is an emerging field at the intersection of statistical learning and programming languages. An appealing property of probabilistic programming languages (PPL) is their support for constructing arbitrary probability distributions. This allows one to model many different domains and solve a variety of problems. We show the link between probabilistic planning and PPLs by i...