If you instead set the PoolGap parameter to value 0.2, the MIP solver would discard any solutions whose objective value is worse than 120 (which would also leave 3 solutions in the solution pool). If you set the PoolSearchMode parameter to 2 and the PoolSolutions parameter to 10, the ...
We strongly recommend that you only bound the result from above. That is, you should avoid using the resultant in situations where the model incentivizes a larger value. This would include situations where the objective coefficient is negative, as well as situations where a larger value for the...
If provided, this value specifies the absolute objective degradation when doing hierarchical multi-objective optimization. The default value is 0. name (optional) Specified via model.multiobj(i).name. If provided, this string specifies the name of the i-th objective function. Note that when ...
An ABS constraint r=abs{x} states that the resultant variable r should be equal to the absolute value of the argument variable x. Arguments: resvar –The resultant variable of the new constraint. argvar –The argument variable of the new constraint. name –Name for the new general constrain...
wi=50 should incur a penalty of 10 in the objective function If this is the case, I don't see a reason to introduce the min and max functions; your objective function is equivalent to minimizing the sum over all i∈M of the absolute values of wi−60: min ∑i∈M...
of an integer programming model where all the decision variables in the constraint are binary, the goal is to find another constraint involving the same binary variables that is logically equivalent to the original constraint, but that has the smallest possible absolute value of the right-hand ...
If provided, this value specifies the absolute objective degradation when doing hierarchical multi-objective optimization. The default value is 0. name (optional) Specified via model$multiobj[[i]]$name. If provided, this string specifies the name of the i-th objective function. Note that when ...