The traditional linear programming model is deterministic. The way that uncertainty is handled is to compute the range of optimality. After the optimal solution is obtained, typically by the simplex ...

Linear and nonlinear programming are two types of optimization methods that can help you find the best solution to a problem involving decision variables, constraints, and an objective function.

In this paper, we focus on two-level linear programming problems involving random variable coefficients in objective functions and/or constraints. Using chance constrained programming techniques, the ...

This paper deals with the multi-objective chance constrained programming, where the right hand side of the constraints are normally distributed and the objective functions with fuzzy numbers ...

Sensitivity Analysis: Explore how changes in coefficients of the objective functions and constraints affect the Pareto optimal solutions. Multi-Objective Expansion: Extend the problem to include ...

Linear programming is appropriate for this problem as the constraints and objective function are linear in form (i.e. They can be expressed as linear combinations of decision variables and ...

Solving linear programming problems in a spreadsheet yields equations for maximizing business profits. As applied to business, linear programming typically involves an objective formula, such as ...

Explore a realistic approach to linear programming with random coefficients and risk analysis. Discover how to design a copula of objective function coefficients for business analytics and risk ...