Multi Variable Optimization Problem

In summary, the problem is to optimize the pressurization of 20 balloons in a line with a known function of pressure and volume, given constraints on injection rates, pressure differentials, and leakage. The objective is to minimize the total amount of time while ensuring that the pressure in each balloon reaches Pmax. There are also additional constraints on the number of balloons being pressurized simultaneously and the total air supply capacity. However, finding a single analytical solution may be difficult due to various subclasses of the problem.
  • #1
geetar_king
26
0
I have a problem that I normally find solutions to via trial and error, and they usually aren't optimized, but was wondering if there is a better way to solve this and optimize.

My application is specific but this is the best way I can describe the problem. Forgive me if it doesn't make sense!
---

There is a set of 20 balloons laid out in a line. Each balloon initially begins with the same pressure and each has the same volume. All balloons are materially the same.

Now imagine there is a restrictive port connecting Balloon #1 to Balloon #2, Balloon #2 to #3, #3 to #4... ...#18 to #19, and #19 to #20.

This port allows air to pass between the balloons, where the leakage rate from balloon i to j is Lij and is a rate that is a known function of to the pressure differential between balloons.

There is also another port on each balloon, an 'injection port', which connects to the air supply manifold.

Objective/Problem:
The goal is to pressurize all balloons up to Pmax in the quickest amount of time subject to the folllowing constraints.​

Constraints:
  • The Pressure in balloon #i is a known function of volume in the balloon: Pi = f(Vi)
  • Maximum injection rate into any balloon is Pmax.
  • Minimum injection rate into any balloon is Pmin, otherwise zero injection rate.
  • There is a total air supply capacity, C, so that all injection rate must sum to C (maximize capacity usage)
  • A number, N, balloons must be on injection simultaneously at all times in a block formation (ie group of N balloons side by side).
  • The maximum pressure differential between any balloons is D.
  • Leakage, Lij, as described above is small compared to injection rate. (could consider zero to start with if this complicates things.)

Optimization:
Minimize the amount of 'fillup volume' on each balloon before the balloon reaches Pmax.​




...

So normally I start with a 20 column 2D grid with injection rates in each row at each time for each balloon, and shift rates around and then see what pressure is and try to work with the constraints.

I'm not sure if there is a way to do this mathematically or with matrix operations or something...

Let me know if there are too many unknowns or if something doesn't make sense.

Thanks
 
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  • #2
typo fixed

geetar_king said:
I have a problem that I normally find solutions to via trial and error, and they usually aren't optimized, but was wondering if there is a better way to solve this and optimize.

My application is specific but this is the best way I can describe the problem. Forgive me if it doesn't make sense!
---

There is a set of 20 balloons laid out in a line. Each balloon initially begins with the same pressure and each has the same volume. All balloons are materially the same.

Now imagine there is a restrictive port connecting Balloon #1 to Balloon #2, Balloon #2 to #3, #3 to #4... ...#18 to #19, and #19 to #20.

This port allows air to pass between the balloons, where the leakage rate from balloon i to j is Lij and is a rate that is a known function of to the pressure differential between balloons.

There is also another port on each balloon, an 'injection port', which connects to the air supply manifold.

Objective/Problem:
The goal is to pressurize all balloons up to Pmax in the quickest amount of time subject to the folllowing constraints.​

Constraints:
  • The Pressure in balloon #i is a known function of volume in the balloon: Pi = f(Vi)
  • Maximum injection rate into any balloon is Qmax.
  • Minimum injection rate into any balloon is Qmin, otherwise zero injection rate.
  • There is a total air supply capacity, C, so that all injection rates must sum to C (maximize capacity usage)
  • A number, N, balloons must be on injection simultaneously at all times in a block formation (ie group of N balloons side by side).
  • The maximum pressure differential between any balloons is D.
  • Leakage, Lij, as described above is small compared to injection rate. (could consider zero to start with if this complicates things.)

Optimization:
Minimize the amount of 'fillup volume' on each balloon before the balloon reaches Pmax.​




...

So normally I start with a 20 column 2D grid with injection rates in each row at each time for each balloon, and shift rates around and then see what pressure is and try to work with the constraints.

I'm not sure if there is a way to do this mathematically or with matrix operations or something...

Let me know if there are too many unknowns or if something doesn't make sense.

Thanks
 
  • #3
There is presumably also a constraint on the number of times the "blocks" of balloons are exchanged (e.g. a amount of time to perform the exchange)? And on the frequency of change of Qi? Otherwise a arbitrarily large number of changes can be performed to trivially achieve an optimal solution.

Your optimization function "Minimize the amount of 'fillup volume' on each balloon before the balloon reaches Pmax." does not make sense as the "fillup volume" for each balloon is presumably given by the inverse of the function P = f(V) where P = Pmax. Presumably, in line with the statement of the "Objective/Problem" the optimisation function is simply to minimise the total amount of time?

There are a number of subclasses of this problem which defy a single analytical solution:
  • N = 1, 2, 4, 5, 10 - the balloons can be charged in blocks until the pressure differential between blocks reaches D
  • D > Pmax / N (ignoring Lij) in which case an optimal solution is trivial by filling B1 to Pmax, B2 to Pmax x (N-1)/N etc.
  • Where Qmax < C / N and Qmin > C / N things get harder.

I think you need to narrow down the scope of the problem a little.
 

Related to Multi Variable Optimization Problem

1. What is a multi variable optimization problem?

A multi variable optimization problem is a mathematical problem where the goal is to find the optimal values for multiple variables, taking into account various constraints and objectives. This type of problem often arises in fields such as engineering, economics, and computer science, where there are multiple factors that need to be optimized for the best outcome.

2. What are some common techniques used to solve multi variable optimization problems?

Some common techniques used to solve multi variable optimization problems include gradient descent, genetic algorithms, simulated annealing, and linear programming. Each method has its own advantages and disadvantages, and the most appropriate technique will depend on the specific problem at hand.

3. What is the difference between a local and a global optimum in multi variable optimization?

A local optimum is a solution that is the best within a specific region of the problem space, but may not be the overall best solution. A global optimum, on the other hand, is the best possible solution for the entire problem. In multi variable optimization, it is important to identify the global optimum, as it represents the best possible outcome for all variables.

4. How can multi variable optimization problems be applied in real life?

Multi variable optimization problems have a wide range of applications in real life, including in engineering design, resource allocation, financial planning, and data analysis. These problems are often used to maximize efficiency, minimize costs, or find the best solution in complex scenarios with multiple variables and constraints.

5. What are some common challenges when solving multi variable optimization problems?

Some common challenges when solving multi variable optimization problems include dealing with a large number of variables, handling non-linear relationships between variables, and finding the global optimum instead of getting stuck at a local optimum. Additionally, identifying and incorporating all relevant constraints and objectives can also be a challenge in these types of problems.

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