Complex Optimization Problem regarding Areas

In summary, the problem involves finding the optimal number of firebreaks in a 50km x 50 km square forest to minimize the area lost in a fire. In part 1, the firebreaks are evenly spaced and parallel to each other and one edge of the forest. In part 2, the firebreaks are arranged in two equally spaced sets of parallel lines. The equations used to solve the problem are the area of the burned forest, the area of the firebreaks, and the area saved. The derivative of the area saved equation is used to find the critical number, which is shown to be 5000. The solution to part 2 is found to be 17 firebreaks in both the north
  • #1
Remington
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Homework Statement


Part 1:
A forest in the shape of a 50km x 50 km square has firebreaks in rectangular strips 50km by 0.01 km. The trees between two fire breaks are called a stand of trees. All firebreaks in this forest are parallel to each other and to one edge of the forest, with the first firebreak at the edge of the forest. The firebreaks are evenly spaced throughout the forest. The total area lost in the case of a fire is the area of the stand of trees where the fire started and the combined area of the firebreaks.

Part 2:
Now suppose that the firebreaks are arranged in two equally spaced sets of parallel lines, as shown in Fig 2. The forest is again 50km x 50km and each break is 50km x 0.01km. Find the optimal number of firebreaks in each direction to minimize the area lost due to the fire. Assume that there must be the same number of firebreaks in each direction. (eg. 3 North to South and 3 East to West). Make sure you do not count the area of overlap in the firebreaks more than once. Assume that you know the critical number is 5000 for part 2 in order to find the value of n we are interested in.

The Figures: http://i42.tinypic.com/3324bk3.jpg

Homework Equations



Here are the equations I have came up with.
Domain for n is [1,5000]

Area of Burned Forest = Af(n) = ((50/n)-0.01)^2
Area of Firebreaks = Ab(n) = 2n^2((1/2n)-0.0001)-0.0001n^2
Area saved = As(n) = 2500 - [Af(n) + Ab(n)]
d/dx As = 5000-n-(n^4)/5000+2n^3

The Attempt at a Solution


What I've been trying to do is find the critical numbers of the equation from the derivative.
Our professor gave the the critical number as 5000, but 5000 is the max amount of fire breaks you can have, as 5000x0.01km = 50km. I know the solution is 17 firebreaks from a maximum of 17.09 I obtained from a graph. I just cannot seem to get there with the math.
When I emailed my professor he told me:
You need to take the derivative of your Area function to find the critical numbers and you need to show that the value you find is actually a minimum. If you simplify your function it will be easier to take the derivative. Multiply terms out and get the function in its simplest form before taking the derivative.

I simplified the equations but the values I'm getting are still outside of the domain except for 5000. I can prove that 5000 is a minimum but I don't see how that leads to a solution.

I have solved part 1, I'm just stuck at part 2.

Links to my work if the text is hard to read.
http://i39.tinypic.com/qxq4ww.jpg - Part 2a
http://i41.tinypic.com/16le9fq.jpg - Part2b
 
Last edited:
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  • #2
Remington said:
Area of Burned Forest = Af(n) = ((50/n)-0.01)^2
Area of Firebreaks = Ab(n) = 2n^2((1/2n)-0.0001)-0.0001n^2
Area saved = As(n) = 2500 - [Af(n) + Ab(n)]
d/dx As = 5000-n-(n^4)/5000+2n^3

I know you can't use this now as you would be way past due :redface:, but here's to help anyone else who has seen the same problem.
I came up with a different equation:
A1 burned area(n)=((50/.5n)-.01)2=((100/n)-.01)2=10000n-2-2n-1+.0001 Because half of the firebreaks are n/s and the other half are e/w, you account for .5 of the total firebreaks for each side of burnout areas.
Atotal firebreaks(n)=0.5n-.0001(0.5n)2=.5n-.000025x2 Take the area of 1 firebreak (.5km2) minus the overlap areas (.0001km2). The overlap areas are only subtracted from the verticle OR horizontal firebreaks, not both.
Asaved=2500-(A1 burned area+Atotal firebreaks) This function gives you total firebreaks (x axis) vs total area saved (y axis). Either graph it out and find the max button on your calculator, or find the derivative and set to 0. I will break down the derivative.
d/dn (2500-10000n-2+2n-1-.0001.-.5n+.000025n2)=0
20000n-3-2n-2-.5+.00005n=0, x=34.1995
Therefore there are 17 north/south firebreaks, and 17 east/west firebreaks.

I hope this helped anyone looking for an answer to this problem, and any feedback would be welcomed.
 

FAQ: Complex Optimization Problem regarding Areas

1. What is a complex optimization problem regarding areas?

A complex optimization problem regarding areas involves finding the maximum or minimum value of a function that depends on multiple variables, subject to a set of constraints, in order to optimize the area of a given shape or region.

2. What types of constraints are typically involved in these problems?

Constraints in complex optimization problems regarding areas can include geometric constraints such as perimeter, angles, and side lengths, as well as physical constraints such as budget or resource limitations.

3. How is this type of problem solved?

Complex optimization problems regarding areas can be solved using various mathematical techniques, such as calculus, linear programming, or dynamic programming. Computer algorithms, such as genetic algorithms or simulated annealing, can also be used to find solutions.

4. What are some real-life applications of complex optimization problems regarding areas?

These types of problems are commonly encountered in fields such as engineering, architecture, and operations research. For example, optimizing the layout of a factory floor or designing the most efficient irrigation system for a farm.

5. What are the challenges in solving complex optimization problems regarding areas?

One of the main challenges is the large number of variables and constraints involved, which can make the problem computationally intensive and time-consuming to solve. Additionally, finding the global optimum can be difficult, as the solution space can be highly nonlinear and complex.

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