List of quantitative methods for optimization

In summary, the purpose of creating a list of quantitative methods for optimization is to provide a comprehensive and organized resource for researchers and practitioners to select and apply appropriate techniques for solving optimization problems. Common types of quantitative methods for optimization include linear programming, nonlinear programming, integer programming, dynamic programming, and genetic algorithms. Researchers typically choose a method based on the problem's characteristics, and these methods can be combined to solve more complex problems. However, using quantitative methods for optimization may have limitations, such as requiring significant computational resources and expertise in mathematical concepts, and may not accurately reflect real-world scenarios.
  • #1
Bobishere
1
0
Max: 3x + 5y
s.t. x + 2y ≤ 5
x ≤ 3
y ≤ 2
x,y ≥0

By the simplex method, the profit is $14. Using sensitivity analysis I changed the RHS of the 1st constraint and keeping everything else constant, I get the best profit value of $19 at RHS of 7.

What other methods can I use such as the sensitivity analysis to see the improvement in my profit? Please provide me with a list.

Thanks
 
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  • #2
The improvement in profit if you change what? With the given constraints, 14 is clearly the maximum.
 

1. What is the purpose of creating a list of quantitative methods for optimization?

The purpose of creating a list of quantitative methods for optimization is to provide researchers and practitioners with a comprehensive and organized resource for selecting and applying appropriate techniques to maximize their efficiency and effectiveness in solving optimization problems.

2. What are some common types of quantitative methods used for optimization?

Some common types of quantitative methods used for optimization include linear programming, nonlinear programming, integer programming, dynamic programming, and genetic algorithms.

3. How do researchers determine which quantitative method to use for optimization?

Researchers typically determine which quantitative method to use for optimization based on the characteristics of the problem they are trying to solve, such as the number of variables and constraints, the linearity or nonlinearity of the objective function, and the presence of integer or continuous variables.

4. Can these quantitative methods be combined or used in conjunction with each other?

Yes, these quantitative methods can be combined or used in conjunction with each other to solve more complex optimization problems. For example, a genetic algorithm may be used to generate initial solutions for a linear programming problem, which can then be refined using dynamic programming.

5. Are there any limitations or drawbacks to using quantitative methods for optimization?

While quantitative methods can be powerful tools for solving optimization problems, they do have some limitations. For example, they may require a significant amount of computational resources or assume certain simplifying assumptions that may not accurately reflect real-world scenarios. Additionally, the selection and application of these methods may require a certain level of expertise and understanding of mathematical concepts.

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