This function creates a goal which instantiates all variables in the model. The goal is not the same as automatic search, although it uses in part the same underlying procedures. In particular it does not use restarts, multi-point search, etc.

This goal must be used in conjunction with a user-defined goal. Your user-defined goal solves the difficult part of the problem but it can leave some variables unfixed (in particular it can leave as unfixed variables which can be instantiated trivially or almost trivially). Then simple completion goal fixes all these variables.

The simple completion goal works in the following steps:

• It first instantiates all interval variables. Depending on the objective function and IloCP::TemporalRelaxation parameter it use either chronological search or temporal relaxation.
• It instantiates all integer variables in the same way as IlcInstantiate(allIntegerVariables, IlcChooseMinSizeInt).
• It instantiates all sequence variables (instances of IloIntervalSequenceVar). This may be necessary when there are intervals with zero length in a sequence.
• It instantiates all state functions (instances of IloStateFunction). In case of state function in the model, this goal is mandatory.

The goal is guaranteed to find a solution if there is one, but if the remaining problem is not trivial, it make take a long time. It is recommended to use this goal after your user-defined goal if your search goal does not instantiate all variables in the model. Keep in mind that the remaining problem should be simple to solve. Some examples:

• Your goal decides on precedence for solving critical resources, for example, it sequences all sequence variables, and then lets the simple completion goal assign start and end times of interval variables to achieve the best objective value. Thanks to temporal relaxation inside simple solution goal, it can be quite effective strategy.
• Some intervals or integer variables values are dominated by main decision variables: for example the master of a span or alternative constraint. Similarly, integer or cumul element variable that models some capacity constraints are essentially determined by the variable that consumes the cumul. Your goal does not fix the dominated variables and lets the simple completion goal do it.
• This goal is mandatory if the model contains state functions.