Which Allocation Maximizes Factory Profits?

In summary, the conversation discusses the profits made by factories A, B, and C when selling one unit of material to dealers L, M, N, and P. The output of each factory and demand from each dealer are given, and the goal is to find an allocation that maximizes profits. The suggested approach is to use a transportation algorithm such as the North West Corner Rule or the Minimum Cost Method.
  • #1
ra_forever8
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The table below records the profits that are made when one unit of material is sold by factories A,B, C to dealers L,M,N, P. The output of each factory and demand from each dealer are given in brackets
( A,B,C ) -> L(30) M(30) N(30) P(45)
A(100) -> 25 30 20 20
B(20) -> 30 25 15 10
C(15) -> 10 35 5 30

Find the allocation which maximises the profits for the factories.
(Hint: this is similar to a transportation problem . You need to decide which inequalities should be reversed.)
 
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  • #2
Re: Mathematical Programming

grandy said:
The table below records the profits that are made when one unit of material is sold by factories A,B, C to dealers L,M,N, P. The output of each factory and demand from each dealer are given in brackets
( A,B,C ) -> L(30) M(30) N(30) P(45)
A(100) -> 25 30 20 20
B(20) -> 30 25 15 10
C(15) -> 10 35 5 30

Find the allocation which maximises the profits for the factories.
(Hint: this is similar to a transportation problem . You need to decide which inequalities should be reversed.)

Hi grandy, :)

You have to subtract each element in the table from the largest element of the table and apply a transportation algorithm such as the North West Corner Rule or the Minimum Cost Method. The following article will give you a good description about each of the transportation algorithms.

http://homes.ieu.edu.tr/~ykazancoglu/BA228/Transportation.pdf

You can check your solution >>here<<.

Kind Regards,
Sudharaka.
 

FAQ: Which Allocation Maximizes Factory Profits?

What is Mathematical Programming II?

Mathematical Programming II is a subfield of operations research that uses mathematical models and algorithms to solve optimization problems. It involves the use of mathematical techniques and computer programs to make optimal decisions in planning and resource allocation.

What are some common applications of Mathematical Programming II?

Mathematical Programming II has a wide range of applications, including supply chain management, scheduling and routing problems, financial portfolio optimization, and production planning. It is also commonly used in engineering, economics, and social sciences.

What are the main differences between Mathematical Programming I and II?

The main difference between Mathematical Programming I and II is the complexity of the optimization problems being solved. Mathematical Programming I deals with linear programming, which involves optimizing a linear objective function subject to linear constraints. Mathematical Programming II deals with nonlinear programming, which involves optimizing a nonlinear objective function subject to nonlinear constraints.

What are some common techniques used in Mathematical Programming II?

Some common techniques used in Mathematical Programming II include gradient descent, Newton's method, and interior point methods. These methods use iterative processes to find the optimal solution to nonlinear optimization problems.

How is Mathematical Programming II used in real-world situations?

Mathematical Programming II is used in real-world situations to make optimal decisions in a variety of industries. For example, it can be used to optimize production schedules in manufacturing, to determine the most efficient routes for transportation and logistics, and to allocate resources in project management. It is also used in financial planning to optimize investment portfolios and in environmental management to find the most sustainable solutions.

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