What's the better what to add constrains in OptaPlanner









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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







share|improve this question



























    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







    share|improve this question

























      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







      share|improve this question















      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






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      edited Nov 10 at 20:15

























      asked Nov 10 at 20:08









      VibeeshanRC

      3,29654085




      3,29654085






















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          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.






          share|improve this answer




















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            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.






            share|improve this answer
























              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.






              share|improve this answer






















                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.






                share|improve this answer












                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.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Nov 13 at 16:44









                Geoffrey De Smet

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