Mathematical Modeling with Optimization, Part 3: Problem-Based Mixed-Integer Linear Programming

From the series: Mathematical Modeling with Optimization

Alan Weiss, MathWorks

See the steps of a problem-based approach for modeling and solving a steel blending problem. A variety of raw material may be purchased, but some are only available in fixed quantities. This restriction results in a model that is a mixed-integer linear program. Use Optimization Toolbox™ to interactively define the optimization problem, optimization variables, and constraints. Review the problem after each addition using the problem, variable, and constraint display functions. Solve the problem and display the results.

Part 1: From Problem Description to Mathematical Program Transform a problem description into a mathematical program that can be solved using optimization, using a steam and electric power plant example.

6:04

Part 2a: Problem-Based Linear Programming Solve a linear program with Optimization Toolbox solvers and a problem-based approach, using a steam and electric power plant example.

10:46

Part 2b: Solver-Based Linear Programming Solve a linear program using Optimization Toolbox solvers, using a steam and electric power plant example.

4:31

Part 3: Problem-Based Mixed-Integer Linear Programming Through a steel blending example, you will learn how to solve a mixed-integer linear program using Optimization Toolbox solvers and a problem-based approach.

5:15

Part 4: Problem-Based Nonlinear Programming Express and solve a nonlinear optimization problem with the problem-based approach of Optimization Toolbox. Use nonlinear functions in both the objective function and constraints. Solve with an automatically selected solver.