Simplex Method - Programming Problem

In summary, the student is trying to solve a linear programming problem with three constraints, but is unsure of how to do it. They attempted to do it using the Simplex algorithm but it didn't work perfectly. They then consulted a textbook and found that they needed to add one slack variable for each of the constraints. After doing that, they graphed the feasible region and found that the point they found maximized the objective function.
  • #1
Maatttt0
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0
Simplex Method --- Programming Problem

Homework Statement



Here's the question -
Conside the linear programming problem:

maximise P = -3x + y,

subject to 3x + 2y =< 24,
4x + 9y =< 36,
-2x + y =< 1,
and x >= 0, y >= 0.

Represent the problem as an initial Simplex tableau and use the Simplex algorithm to solve the problem. Interpret your final tableau.


Homework Equations





The Attempt at a Solution



My problem is I'm usure how to tackle this with 3 subjects (ignoring the one with X and Y must be greater or equal to 0). I have completed questions in class with 2 subjects but my teacher nor my textbook mention anything about 3 of them.

I attempted it a couple of times with just putting it in a follow the steps as I would with 2 subjects but it doesn't seem to work perfectly. Is this the correct way?

I would post my attempts but it'll get a bit complicated over the computer.

Thank you in advance :)
 
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  • #2


The three inequalities are called constraints, not subjects. The idea is to maximize the objective function, subject to those constraints.

To get this ready to put into a simplex tableau, you need to add one slack variable for each of the three inequalities that involve both x and y. This turns each inequality into an equation.

For example, the first inequality becomes 3x + 2y + r = 24.
 
  • #3


Thank you for the reply but I understand that much. I'm unsure if there is a different idea for 3 constraints (I forgot the word earlier).
 
  • #4


I think I have worked it now - thank you all. :)
 
  • #5


No, it's the same no matter how many constraints you have - one row in the tableau for each constraint equation (i.e., the equation you get after adding a slack variable to the constraint inequality).

Once you get an answer, you can check by graphing the feasible region and confirming that the point you found maximizes the objective function.
 
  • #6


Thank you for clearing that up Mark :)
 

1. What is the Simplex Method?

The Simplex Method is an algorithm used to solve linear programming problems. It is a step-by-step process that involves finding the optimal solution to a mathematical model by iteratively improving the objective function value.

2. What is a programming problem?

A programming problem is a mathematical model that involves optimizing a linear objective function subject to a set of linear constraints. It can be represented as a system of linear equations and inequalities, and the goal is to find the best possible solution that maximizes or minimizes the objective function.

3. How does the Simplex Method work?

The Simplex Method works by starting with an initial feasible solution and then moving from one solution to another, improving the objective function value each time. It does this by identifying the most promising vertex in the feasible region and moving towards it until the optimal solution is reached.

4. What are the basic steps of the Simplex Method?

The basic steps of the Simplex Method are:

  1. Formulate the mathematical model and write it in standard form.
  2. Identify the initial feasible solution.
  3. Choose the pivot element, which is the most promising vertex in the feasible region.
  4. Perform row operations to create a new basic feasible solution.
  5. Repeat steps 3 and 4 until the optimal solution is reached.

5. What are the advantages of using the Simplex Method?

The Simplex Method has several advantages, including:

  • It is a well-established and widely-used algorithm for solving linear programming problems.
  • It is relatively easy to understand and implement.
  • It guarantees to find the optimal solution, if one exists.
  • It can handle large and complex problems.
  • It can be used to solve a variety of real-world problems in different fields such as business, economics, and engineering.

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