Linear Programming Exam Question

In summary, the conversation discusses a problem in which a computer manufacturer wants to minimize costs by producing a specific number of Alpha 4 and Beta 5 computers, while also adhering to constraints such as no overtime and a certain number of technicians working a set number of hours. The L.P.P is formulated and put into canonical form, but the origin is not a feasible point due to negative values. The introduction of two artificial variables is needed to solve the problem using the two stage simplex algorithm. However, it is unclear how to introduce these variables. The question may be pointless as it seems that producing any additional computers will lead to a negative income.
  • #1
elyttle
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Homework Statement


Here is the stated problem:

MSA computer corporation manufactures two computer system. Alpha 4 and Beta 5. The firm employs five technicians working 160 hours per month. Management insists on no overtime next month (less than or equal to 160 hours).20 hours labour is required for an Alpha 4, and 25 hours labour required for a Beta 5. MSA wants to see at least 10 Alpha 4s, and 15 Beta 5s produced in the next month. Alpha 4s cost 1200 euro, Beta 5s cost 1800 euro. Determine the number of each to be produced in order to minimize costs.

1)Formulate the above L.P.P

2)Put it into canonical form and show that the origin is not a feasible point.

3)Using two artificial variable x6 and x7 solve the above L.P.P using the two stage simplex algorithm.



The Attempt at a Solution



I have parts 1) and 2) done

z=monthly costs
x1=No of alpha 4 systems
x2=No of beta 5 systems

Alpha 4 costs 1200 euro, Beta 5 costs 1800 euro, so the objective function is

z=1200x1 + 1800x2

5 technicians work 160 hours so there are 800 hours total available. Alpha 4 takes 20 hours and beta 5 takes 25 hours, We also want at least 10 Alpha 4s and 15 Beta 5's. SO our contraint equations are:

20x1+25x2≤800
x1≥10
x2≥15

and our non-negative contraints, x1≥0 and x2≥0

So the full L.P.P is...

Minimize z=1200x1 + 1800x2

subject to 20x1+25x2≤800
x1≥10
x2≥15

x1≥0 , x2≥0

To put the L.P.P in canonical form we introduce slack variables and change the objective function to maximize, so canonical form is

Maximize z= -1200x1 - 1800x2

subject to 20x1+25x2 + x3 = 800
x1 - x4 = 10
x2 - x5 = 15

x1≥0 , x2≥0 , x3≥0 , x4≥0 , x5≥0



The origin is clearly not a feasible point because at x1 = 0 and x2 = 0, x3 = 800, x4 = -10, x5 = -15

Negative values are present so the origin is not feasible.

Im getting stuck now because I don't know how to introduce the artificial variables x6 and x7, do we not need at least three artificial variables, one for each constraint, if not which constraints should I add the artificial variables to? A little help would be much appreciated.
 
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  • #2
I don't understand the question, or the question is pointless. Clearly the workers have to produce 10 Alpha 4s and 15 Beta 5s. After that, each additional computer leads to more costs, so we just stop building computers. No overtime has to be paid and the minimal amount of computers was built, so all constraints are satisfied and costs are minimal.

Shortly afterwards, the manufacturer will go bankrupt, as producing anything seems to generate a negative income.
Are we supposed to maximize costs (with the implication that more produced computers will also give more revenue when they are sold)? That would lead to something to calculate.

Why don't you introduce variables for x1-10 and x1-15 and similarly for the costs? That simplifies the problem.
 
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Related to Linear Programming Exam Question

1. What is linear programming and why is it important?

Linear programming is a mathematical optimization technique used to find the best possible solution to a problem with linear constraints. It is important because it can be applied to a wide range of real-world problems, such as resource allocation, production planning, and transportation planning, to help find the most efficient and cost-effective solution.

2. How is a linear programming problem formulated?

A linear programming problem is formulated by defining the objective function, which represents the goal to be achieved, and the constraints, which are the limitations or restrictions on the variables involved. These variables can be optimized using mathematical techniques to find the optimal solution.

3. What is the difference between primal and dual problems in linear programming?

The primal problem in linear programming involves maximizing or minimizing the objective function subject to constraints, while the dual problem involves minimizing or maximizing the same objective function under different constraints. The solutions to both problems are related and can be used to check the accuracy of the other.

4. What are the assumptions made in linear programming?

The main assumptions made in linear programming include:

  1. The objective function and constraints are all linear.
  2. The variables involved are continuous and can take on any real value.
  3. The problem has a feasible solution.
  4. The problem has a finite optimal solution.

5. What are the different types of linear programming techniques?

There are several types of linear programming techniques, including the simplex method, the interior-point method, and the branch and bound method. Each technique has its own advantages and can be used to solve different types of linear programming problems.

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