ABSTRACT: By using the penalty function method with objective parameters, the paper presents an interactive algorithm to solve the inequality constrained multi-objective programming (MP). The MP is ...

Linear programming is a powerful technique for optimizing a linear objective function subject to a set of linear constraints. One of the simplest and most intuitive ways to solve a linear ...

The approach to randomized objective function linear programming presented here is well suited to address the issue of uncertainty in risk management. Instead of focusing on short term profits ...

A standard linear programming code may be used to compute optimal trajectories for a linear discrete-time system with respect to a minimax criterion on either state or control trajectories. Arbitrary ...

Utilize the weighted average method or the lexicographic method to find a Pareto optimal solution for the given bi-objective linear program. Modified Objective Functions: In a revised version of the ...

A quantum framework for Combinatorial Optimization Problem with linear objective functions. It also can solve graph problem if it's optimization objective function can be represented as linear ...

As in all linear programming models, you first create linear inequalities out of the information you have about any constraints. In the case of profit maximisation or loss minimisation, for example, ...

The conventional Linear Programming (LP) model requires the parameters to be known as constants. In the real world, however, the parameters are seldom known exactly and have to be estimated. Interval ...

Combining Excel's COUNTIF function and graphing tools produces a tally chart. Getty Solving linear programming problems in a spreadsheet yields equations for maximizing business profits.

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.