Optimizing Productivity: Linear Programming for All-Easy's Production Goals

In summary: You can pick any three of the four points to form a triangle s.t. the profit is maximized. You have to do a little work to find which three and how to label them.In summary, All-Easy manufactures three products with unit profits of $1, $9, and $5. The company has budgeted 70 labor hours and 45 machine hours for production. Each product has specific labor and machine time requirements. If the budgeted hours are exceeded, there is an additional cost of $15 per labor hour and $5 per machine hour. To formulate this problem as an LP model, the variables x, y, and z can represent the number of units of products A, B, and C,
  • #1
franz32
133
0
All-Easy manufactures three products whose unit profits are $1, $9 and $5, respectively. The company has budgeted 70 hrs. of labor time
and 45 hours of machine time for the production of three products.
The labor requirements per unit of products A,B C are 2, 3 and 5 hours, respectively. The corresponding machine time requirements per unit are 1, 4 and 5 hour.

All-Easy regards the budgeted labor and machine hours as goals that must be exceeded, if necessary,but at the additional cost of $15 per labor hour and $5 per machine hour. Formulate the problem as an LP model.

Doubts w/ solutions:

I let x = no. of units of product A, y = no. of units of product B, z = no. of units of product C.

Maximize: z = x + 9y + 5z (profit)
subject to:
2x + 3y + 5z <= 70 (labor hrs.)
x + 4y + 5z <= 45 (machine hrs.)
x,y,z >= 0

"All-Easy regards the budgeted labor and machine hours as goals that must be exceeded, if necessary, but at the additional cost of $15 per labor hour and $5 per machine hour." - if I were to make mathematical model out of these, am i going to adjust my objective function or my constraints or both? How?
 
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  • #2
Let u = additional labor hours, and v = additional machine hours. How do they affect your profit, and how do they affect your constraint functions? Express it algebraically.
 
  • #3
I got this idea... so at least, I can show you where am I... got stuck

We have a constraint on Labor: 2x + 3y + 5z <= 70,
but it can be exceeded ... at extra cost.

If 2x + 3y + 5z is greater than 70, it costs an additional $15/hour.

The excess is: (2x + 3y + 5z - 70) hours which costs $15/hr.
The extra labor cost is: 15(2x + 3y + 5z- 70) dollars,
which, of course, reduces the profit.


Similarly, we have a constraint on Machine time: x + 4y + 5z <= 45
which can be exceeded ... at extra cost.

The excess is (x + 4y + 5z - 45) hours which costs $5/hr.
The extra machine cost is: 5(x + 4y + 5z - 45) dollars,
which also reduces the profit.

is this correct? Can I relate it to what you've replied... and um, that's it, how will I re-formulate my LP model?
 
  • #4
OK, but remember that your labor hours are no longer limited to 70.
franz32 said:
Maximize: z = x + 9y + 5z (profit)
subject to:
2x + 3y + 5z <= 70 (labor hrs.)
x + 4y + 5z <= 45 (machine hrs.)
x,y,z >= 0
I would also not use "z" to represent profit, since you are already using it for product C. :)

How about:
Maximize: P = x + 9y + 5z - 5u - 15v (profit)
subject to:
2x + 3y + 5z <= 70 + u (labor hrs.)
x + 4y + 5z <= 45 + v (machine hrs.)
x,y,z,u,v >= 0

And if the goals must be exceeded, you have actual equality:
2x + 3y + 5z = 70 + u (labor hrs.)
x + 4y + 5z = 45 + v (machine hrs.)
Solving for u and v in those get you the two relationships you mention in your post.
 
  • #5
Um... I did understand about it... but if I were to write the final part as my model for the LP, it seems that it is "unstable"... bec. I am following the standard form of a LP model...
 
  • #6
The final part? you mean the two equalities?
 
  • #7
yes.. bec. I don't feel that my model is a formal one yet... =)
 
  • #8
The equalities are the bounding surfaces, and the solution is found on the surface--the vertices, in fact.
 

Related to Optimizing Productivity: Linear Programming for All-Easy's Production Goals

1. What is linear programming and how does it relate to productivity optimization?

Linear programming is a mathematical optimization technique used to find the best possible solution for a given problem that has multiple variables and constraints. In the context of productivity optimization, linear programming can be used to determine the most efficient allocation of resources for achieving a company's production goals.

2. What are the benefits of using linear programming for productivity optimization?

Linear programming allows for a systematic and objective approach to finding the most optimal solution for a complex problem. It also takes into account various constraints and can provide multiple solutions for comparison, giving companies the ability to make informed decisions for productivity optimization.

3. Can linear programming be applied to all types of production goals?

Yes, linear programming can be used to optimize productivity for a wide range of production goals, including increasing output, reducing costs, and maximizing profit. It can also be applied to various industries such as manufacturing, logistics, and service sectors.

4. What factors should be considered when using linear programming for productivity optimization?

When using linear programming for productivity optimization, it is important to consider all relevant variables and constraints, such as resource availability, production capacity, and market demand. It is also crucial to have accurate data and clearly defined goals to ensure the effectiveness of the optimization process.

5. Are there any limitations to using linear programming for productivity optimization?

While linear programming is a powerful tool for productivity optimization, it does have some limitations. It assumes that all data and variables are known and can be accurately measured, which may not always be the case in real-life situations. Additionally, it is only applicable to linear relationships and may not be suitable for highly complex or nonlinear problems.

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