What is the Cheapest Program to Train a Senior Manager with Specific Skills?

In summary, the goal is to train the Senior Manager in skills x1, x2, x3, x4, x5, x6, x7, x8, x9, x10. There are 15 programs available, each with a different combination of skills and cost. The objective is to choose the most cost-effective program(s) that cover all of the Senior Manager's skills. The decision variables are represented by px, with a value of 1 if the program is chosen and 0 if it is not. The constraints are the skills that need to be covered, and the conflicts between certain programs. The output will show the chosen programs and their respective costs.
  • #1
operationsres
103
0
Goal: You want to train your Senior Manager. He needs skills:
x1, x2, x3, x4, x5, x6, x7, x8, x9, x10.

You are to choose from the following programs/courses that fulfills all the senior manager's skill needs at the cheapest cost.
p1 has x1, x3, x4 at $500
p2 has x3, x5, x9, x10 at $1000
p3 has x2 at $ 600
.
.
.
.
p15 has ...


p1 conflicts with p2, p8.
p2 conflicts with p1, p4, p10.
.
.
. (.. This part can probably be inputted into our program later on, if we try and formulate the whole thing at once our brains will probably asplode.)

3. attempt at a solution.

Sorry, no deal! Have no idea how to formulate into a program :)
 
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  • #2
This seems kind of standard for these types of problems, which have the form:

Maximize/Minimize [itex] f(x_1,x_2,...,x_n) [/itex]
subject to:
constraint
...
constraint

You have something you want to minimize/maximize. What is it?

You have constraints. Here you have two types of constraints. What are they? How can you write them down as equations in the usual constraint form?

Then it's just a typical linear program.

Try and work some of it out for us and then we can help you further.
 
  • #3
hgfalling said:
This seems kind of standard for these types of problems, which have the form:

Maximize/Minimize [itex] f(x_1,x_2,...,x_n) [/itex]
subject to:
constraint
...
constraint

You have something you want to minimize/maximize. What is it?

You have constraints. Here you have two types of constraints. What are they? How can you write them down as equations in the usual constraint form?

Then it's just a typical linear program.

Try and work some of it out for us and then we can help you further.

Thankyou for replying.

If xi are the skills, MAXing or MINing f(x1,x2,x3...) doesn't work. The programs/courses (px) will have to be the decision variables. Beyond that and I'm wholly unsure.

I've been trying for over a week, and have gotten zip. I can't do it.
 
  • #4
Yes, that's right. Programs should be the decision variables.

Each one will be a 0 or 1 integer variable, right? If you take that class, then it's 1, if you don't then it's zero.

Now you can write down an expression in terms of px that we are trying to minimize.
 
  • #5
hgfalling said:
Yes, that's right. Programs should be the decision variables.

Each one will be a 0 or 1 integer variable, right? If you take that class, then it's 1, if you don't then it's zero.

Now you can write down an expression in terms of px that we are trying to minimize.

Yep I've gotten this far.

Constaints are confounding me, at the moment.

I have:
MINIMIZE,
z = cost1*p1 + cost2*p2 + cost3*p3 + ... + cost15*p15,

s.t.
"..
..
.."

px = 0 or 1 (Binary).
 
  • #6
Yes, all that is right. Now you have two types of constraints.

One type is that you need to make sure that each skill is covered.
Now suppose we consider the first skill. If px are our decision variables, what equation can we write that ensures that x1 will be covered?

If there are ten skills, that's ten constraints.

The other type of constraint is the conflict of various classes with each other.
Consider the first class. It says that p1 conflicts with p2 and p8. What equation can we write that ensures that we won't take both p1 and p2, for example?

All these should just be in terms of px; there's no need to make explicit reference to the skills at all.
 
  • #7
hgfalling said:
Now suppose we consider the first skill. If px are our decision variables, what equation can we write that ensures that x1 will be covered?

If there are ten skills, that's ten constraints.

SIGMApi1 = 1, where pi1 = all the programs that have skill x1 inside of it.
SIGMApi2 = 1,
.
.

I think you're a genius...

THIS IS WORKING IN LINGO! WQOW!

hgfalling said:
The other type of constraint is the conflict of various classes with each other.
Consider the first class. It says that p1 conflicts with p2 and p8. What equation can we write that ensures that we won't take both p1 and p2, for example?.

Wow that's a real brain buster.

p1 = 2y;
p2 = 2(1-y);

BIN(y);

That doesn't work for this problem though.

HMMMMMM



---
THANKYOU SO MUCH FOR YOUR HELP. Seriously!
 
Last edited:
  • #8
operationsres said:
SIGMApi1 = 1, where pi1 = all the programs that have skill x1 inside of it.
SIGMApi2 = 1,

Almost right. But what if class 1 and class 2 both have skill x1 but are otherwise the most efficient setup because of the other skills they contain and their cost? Then the sum of p1 and p2 will be more than one, but that's okay. This is easily rectified by changing those constraints to inequalities, however.

operationsres said:
p1 = 2y;
p2 = 2(1-y);

BIN(y);

That doesn't work for this problem though.

HMMMMMM


If we did pick two conflicting classes, what would be true of the sum of the p-values of those classes? Write inequalities that prevent us from picking more than one conflicting class.
 
  • #9
hgfalling said:
Almost right. But what if class 1 and class 2 both have skill x1 but are otherwise the most efficient setup because of the other skills they contain and their cost? Then the sum of p1 and p2 will be more than one, but that's okay. This is easily rectified by changing those constraints to inequalities, howeve

You are amazing. I'll deal with the other part of your post after this bit.

"MIN=785.71*p1+2300*p2+900*p3+1325*p4+1200*p5+1150*p6+1450*p7+1166.67*p8+800*p9+1100*p10+985.71*p11+1933.33*p12+828.57*p13+2233.33*p14+2200*p15;

p1+p4+p5+p9>=1;
p1+p4+p5++p7+p9+p10>=1;
p1+p8+p10+p13>=1;
p1+p4+p5+p8+p13>=1;
p1+p9+p12>=1;
.
.
."

Then the middle of the output is like this:

"
10 2.000000 0.000000
11 1.000000 0.000000
12 1.000000 0.000000
13 1.000000 0.000000
14 0.000000 0.000000
15 0.000000 0.000000
16 1.000000 0.000000
17 1.000000 0.000000
18 1.000000 0.000000"

How are some of the constraints 0? I put >=1 ?


If we did pick two conflicting classes, what would be true of the sum of the p-values of those classes? Write inequalities that prevent us from picking more than one conflicting class.

It's presented like this.
...... Programs that Interfere
Program 1... 3 5 8
Program 2... 3 7 10


p1 + p3<=1;
p1 + p5<=1;
p1 + p8<=1;

right?

^^ Infeasible. What the ef :(

Those constraints are correct... wowowowowowow

ROARRRRRRRR
 
Last edited:
  • #10
Hard for me to evaluate why that might be happening, what your software is doing, etc. It seems you are on the right track now, though, as far as formulating the linear program.
 
  • #11
hgfalling said:
Hard for me to evaluate why that might be happening, what your software is doing, etc. It seems you are on the right track now, though, as far as formulating the linear program.

It turns out that I had made mistakes in typing the correct constraints.
I fixed them up, AND IT WORKED, PERFECTLY.

Thank SOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO much2
 

Related to What is the Cheapest Program to Train a Senior Manager with Specific Skills?

1. What is an optimization problem?

An optimization problem is a type of mathematical problem that involves finding the best possible solution to a given situation. The goal is to maximize or minimize a certain quantity, known as the objective function, while considering a set of constraints.

2. What makes an optimization problem hard?

There are a few factors that can make an optimization problem hard, such as a large number of variables, complex constraints, and non-linear objective functions. Additionally, the presence of multiple local optima can make it difficult to find the global optimum.

3. What are some common techniques used to solve optimization problems?

Some common techniques include gradient descent, linear programming, genetic algorithms, and simulated annealing. These methods use different approaches to find the optimal solution based on the specific characteristics of the problem.

4. How are optimization problems used in real-world applications?

Optimization problems have a wide range of applications in various fields, including engineering, economics, and computer science. They can be used to optimize processes, minimize costs, and improve efficiency in areas such as transportation, logistics, and resource allocation.

5. What are some challenges in solving optimization problems?

One of the main challenges in solving optimization problems is the trade-off between accuracy and efficiency. As the complexity of the problem increases, it may require more computational resources and time to find the optimal solution. Another challenge is determining the appropriate objective function and constraints to accurately reflect the real-world scenario.

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