Optimizing Distribution of Goods with Discount Rate: A Simple Problem

In summary: It is helpful for new users and makes it easy to follow.In summary, Gary M. wrote that the optimal distribution for 1200 goods is x0=409.4, x1=400.3, x2=390.3 when p=50 and β=1.1.
  • #1
Gary M.
1
0
I have been attempting this problem for the last 2 hours and 45 minutes with no success and am very frustrated since it should be an extremely easy question. Please help!

The question takes place over 3 time periods (0,1,2). Calculating for a discount rate of 10%, what s the optimal distribution for 1200 goods. Price of the goods are $50. Cost is (Xt^2)/20. B=1+10%=1.1

The equation that I am using is:
V0=pX0-c(X0) + B(pX1-cX1) + B^2(pX2-cX2) s.t. x0+x1+x2=1200
this becomes
p-c'(x0)=B(p-c'x1)-B^2(p-c'x2)
50-x0/10=55-1.1(x1)/10 - 60.5 - 1.2(x2)/10
555=x0-1.1(x1)+1.21(x2)

After that, I've tried everything that I can think of. The answers are supposed to be x0=409.4, x1=400.3, x2=390.3

Please help!
Thanks everyone
 
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  • #2
Gary M. said:
I have been attempting this problem for the last 2 hours and 45 minutes with no success and am very frustrated since it should be an extremely easy question. Please help!

The question takes place over 3 time periods (0,1,2). Calculating for a discount rate of 10%, what s the optimal distribution for 1200 goods. Price of the goods are $50. Cost is (Xt^2)/20. B=1+10%=1.1

The equation that I am using is:
V0=pX0-c(X0) + B(pX1-cX1) + B^2(pX2-cX2) s.t. x0+x1+x2=1200
this becomes
p-c'(x0)=B(p-c'x1)-B^2(p-c'x2)
50-x0/10=55-1.1(x1)/10 - 60.5 - 1.2(x2)/10
555=x0-1.1(x1)+1.21(x2)

After that, I've tried everything that I can think of. The answers are supposed to be x0=409.4, x1=400.3, x2=390.3

Please help!
Thanks everyone

Are you saying that ##C(x_t)= x_t^2/20##? If so, you will have some nonlinearities in the total profit:
[tex] \text{profit} = p x_0 - x_0^2/20 + \beta (p x_1 - x_1^2/20) + \beta^2 (p x_2 - x_2^2/20),[/tex]
which is to be maximized, subject to the constraints ##x_0 + x_1 + x_2 = 1200## AND ##x_0, x_1, x_2 \geq 0##.

One way to solve this is to neglect the inequality constraints ##x_i \geq 0## (and hope they are satisfied anyway), then use a Lagrange multiplier method to deal with the equality constraint. Another way is to reduce it to an unconstrained problem in two variables.

When ##p = 50## and ##\beta = 1.1## the Lagrange multiplier method gives ##x_0 = 390.33, x_1 = 400.30, x_2 = 409.37##, which are the opposite of what you wrote! The Maple package "NLPSolve" and the EXCEL Solver tool also get this solution. Are you sure you are not supposed to have ##\beta = 1/1.1##?
 
Last edited:
  • #3
Gary M,
In future posts, please do not delete the homework template with its three parts.
 

Related to Optimizing Distribution of Goods with Discount Rate: A Simple Problem

1. What is an optimization problem?

An optimization problem is a mathematical or computational problem that involves finding the best solution among a set of possible solutions. The goal is to maximize or minimize a specific objective function while satisfying a set of constraints.

2. What is a simple optimization problem?

A simple optimization problem is one that involves a single objective function and a small number of constraints. It can be solved using simple mathematical techniques or algorithms, and the solution can be easily interpreted and implemented.

3. Why are optimization problems important?

Optimization problems are important because they have a wide range of applications in various fields such as engineering, economics, and operations research. They help in making efficient decisions and finding the best possible solutions to complex problems.

4. What are the steps involved in solving a simple optimization problem?

The steps involved in solving a simple optimization problem include defining the objective function and constraints, determining the feasible region, finding the optimal solution, and verifying the solution. It may also involve using optimization tools and techniques such as linear programming, gradient descent, or genetic algorithms.

5. Can optimization problems have more than one optimal solution?

Yes, it is possible for optimization problems to have multiple optimal solutions. This occurs when the objective function has the same value at different points in the feasible region. In such cases, any of the optimal solutions can be considered as the best solution.

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