What's the better what to add constrains in OptaPlanner
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0
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I am building an OptaPlan solution for an optimization problem.
I have an List<Integer> called weights
These are the only planning variable in my model, all others are shadow.
I want to make sure that sum of weights (weights[0] + weights[1] + weights[2]... = 1000) , While variables other than weights (shadow) will make sure whats the best solution (Using SimpleScore).
Using a simple EasyScoreCalculator might produce a huge set to search for my problem. I believe penalizing score every time for weights[0] + weights[1] + weights[2]... != 1000 is an un-optimized (slow) method.
Whats the best way to do this.
optaplanner
asked Nov 10 at 20:08
VibeeshanRC
3,29654085
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up vote
0
down vote
favorite
I am building an OptaPlan solution for an optimization problem.
I have an List<Integer> called weights
These are the only planning variable in my model, all others are shadow.
I want to make sure that sum of weights (weights[0] + weights[1] + weights[2]... = 1000) , While variables other than weights (shadow) will make sure whats the best solution (Using SimpleScore).
Using a simple EasyScoreCalculator might produce a huge set to search for my problem. I believe penalizing score every time for weights[0] + weights[1] + weights[2]... != 1000 is an un-optimized (slow) method.
Whats the best way to do this.
optaplanner
asked Nov 10 at 20:08
VibeeshanRC
3,29654085
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I am building an OptaPlan solution for an optimization problem.
I have an List<Integer> called weights
These are the only planning variable in my model, all others are shadow.
I want to make sure that sum of weights (weights[0] + weights[1] + weights[2]... = 1000) , While variables other than weights (shadow) will make sure whats the best solution (Using SimpleScore).
Using a simple EasyScoreCalculator might produce a huge set to search for my problem. I believe penalizing score every time for weights[0] + weights[1] + weights[2]... != 1000 is an un-optimized (slow) method.
Whats the best way to do this.
optaplanner
asked Nov 10 at 20:08
VibeeshanRC
3,29654085
I am building an OptaPlan solution for an optimization problem.
I have an List<Integer> called weights
These are the only planning variable in my model, all others are shadow.
I want to make sure that sum of weights (weights[0] + weights[1] + weights[2]... = 1000) , While variables other than weights (shadow) will make sure whats the best solution (Using SimpleScore).
Using a simple EasyScoreCalculator might produce a huge set to search for my problem. I believe penalizing score every time for weights[0] + weights[1] + weights[2]... != 1000 is an un-optimized (slow) method.
Whats the best way to do this.
optaplanner
optaplanner
asked Nov 10 at 20:08
VibeeshanRC
3,29654085
asked Nov 10 at 20:08
VibeeshanRC
3,29654085
edited Nov 10 at 20:15
asked Nov 10 at 20:08
VibeeshanRC
3,29654085
asked Nov 10 at 20:08
VibeeshanRC
3,29654085
asked Nov 10 at 20:08
VibeeshanRC
3,29654085
3,29654085
add a comment |
add a comment |
1 Answer
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oldest
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up vote
1
down vote
accepted
You should penalize the difference between that sum and 1000. So for example, if the weight sum in 920, penalize -80. If the weight it 1019, penalize -19.
Take a look at the cloud balancing problem, I suspect you'll need to just sum all the weights of the items that have @PlanningVariable Boolean included equal to true.
answered Nov 13 at 16:44
Geoffrey De Smet
14.6k53987
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
You should penalize the difference between that sum and 1000. So for example, if the weight sum in 920, penalize -80. If the weight it 1019, penalize -19.
Take a look at the cloud balancing problem, I suspect you'll need to just sum all the weights of the items that have @PlanningVariable Boolean included equal to true.
answered Nov 13 at 16:44
Geoffrey De Smet
14.6k53987
add a comment |
up vote
1
down vote
accepted
You should penalize the difference between that sum and 1000. So for example, if the weight sum in 920, penalize -80. If the weight it 1019, penalize -19.
Take a look at the cloud balancing problem, I suspect you'll need to just sum all the weights of the items that have @PlanningVariable Boolean included equal to true.
answered Nov 13 at 16:44
Geoffrey De Smet
14.6k53987
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
You should penalize the difference between that sum and 1000. So for example, if the weight sum in 920, penalize -80. If the weight it 1019, penalize -19.
Take a look at the cloud balancing problem, I suspect you'll need to just sum all the weights of the items that have @PlanningVariable Boolean included equal to true.
answered Nov 13 at 16:44
Geoffrey De Smet
14.6k53987
You should penalize the difference between that sum and 1000. So for example, if the weight sum in 920, penalize -80. If the weight it 1019, penalize -19.
Take a look at the cloud balancing problem, I suspect you'll need to just sum all the weights of the items that have @PlanningVariable Boolean included equal to true.
answered Nov 13 at 16:44
Geoffrey De Smet
14.6k53987
answered Nov 13 at 16:44
Geoffrey De Smet
14.6k53987
answered Nov 13 at 16:44
Geoffrey De Smet
14.6k53987
answered Nov 13 at 16:44
Geoffrey De Smet
14.6k53987
14.6k53987
add a comment |
add a comment |
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