Linear Programming and Mixed-Integer Linear Programming

• Problem-Based Optimization
  Solve linear programming problems and mixed-integer linear programming problems by creating an optimization problem.
  
• Solver-Based Optimization
  Solve linear programming problems and mixed-integer linear programming problems by defining matrices that represent the objective function and constraints.
  

Featured Examples

Traveling Salesman Problem: Problem-Based

This example shows how to use binary integer programming to solve the classic traveling salesman problem. This problem involves finding the shortest closed tour (path) through a set of stops (cities). In this case there are 200 stops, but you can easily change the nStops variable to get a different problem size. You'll solve the initial problem and see that the solution has subtours. This means the optimal solution found doesn't give one continuous path through all the points, but instead has several disconnected loops. You'll then use an iterative process of determining the subtours, adding constraints, and rerunning the optimization until the subtours are eliminated.

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Optimal Dispatch of Power Generators: Problem-Based

This example shows how to schedule two gas-fired electric generators optimally, meaning to get the most revenue minus cost. While the example is not entirely realistic, it does show how to take into account costs that depend on decision timing.

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Maximize Long-Term Investments Using Linear Programming: Problem-Based

This example shows how to use the problem-based approach to solve an investment problem with deterministic returns over a fixed number of years T. The problem is to allocate your money over available investments to maximize your final wealth. For the solver-based aproach, see .

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