Decision Optimization

Decision Optimization

Delivers prescriptive analytics capabilities and decision intelligence to improve decision-making.


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  • 1.  Static transition times

    Posted 07/15/10 10:50 AM

    Originally posted by: restu


    When I looked at the documentation, the transition times is written like this:
    
    using CP; 
    
    int n = 10; range R = 1..n; tuple triplet 
    { 
    
    int id1; 
    
    int id2; 
    
    int value; 
    }; 
    { triplet 
    } tt = 
    { <i,j, ftoi(abs(i-j))> | i,j in R 
    };
    


    The problem with this is that for each combination i and j, the value will be different, because it is subtracting j from i. For example, when the i = 1, and j = 10, the value will be 9. But when i = 2 and j = 10, the value will be 8.

    What i want to do is to have the value the same every time. So, i tried to write it like this:
    
    
    { triplet 
    } tt = 
    { <i,j, 10> | i,j in R 
    };
    

    So I can have 10 as the value every time. But I got no solution.

    Anyone knows how to make the value static / consistent?

    Thanks in advance.
    #CPOptimizer
    #DecisionOptimization


  • 2.  Re: Static transition times

    Posted 07/15/10 12:34 PM

    Originally posted by: ChrisBr


    Hello,

    The problem doesn't come from the fact that the value is static or not but that your model doesn't have any solution with this value.

    Perhaps an idea would be to try different static values to check when the model has a solution.
    For example, you should check that the sum of the length of all the n interval variables present in the noOverlap constraint plus (n-1)* your_static_value is consistent with the maximum authorized in your model.

    You would check also that you really want such transition times set:
    
    
    { triplet 
    } tt = 
    { <i,j, 10> | i,j in R 
    }
    

    or rather this other one:
    
    
    { triplet 
    } tt = 
    { <i,j, 10> | i,j in R : i!=j 
    };
    


    Hope this helps,

    Chris.
    #CPOptimizer
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