Main > User Guide > Screens > Execute
This screen allows you to perform a number of administrative actions and data manipulations.
User permissions will typically be set to limit access to this screen to key users and/or administrators.
All actions performed from this screen are being logged, and these entries can be seen in the log report from the execute menu.
The actions in brief that can be performed from this screen:
- Generate a plan
This option runs the frePPLe planning engine with the input data from the database. The planning results are exported back into the database.
Two main plan types can be distinguished, based on whether you want to see demand OR material&capacity problems to be shown.- A constrained plan respects all enabled constraints. In case of shortages the demand is planned late or short. No any material or capacity shortages are present in the plan.
- An unconstrained plan shows material, capacity and operation problems that prevent the demand from being planned in time. The demand is always met completely and on time.
- Scenario management
This option allows a user to copy a dataset into a what-if scenario. When the data is successfully copied, the status changes from "free" to "in use".
When the user doesn't need the what-if scenario any more, it can be released again.
The selection box in the upper right corner will display the list of all scenarios that are in use. - Erase the database
This will delete all records from the database.
- Load a predefined dataset in the database
A number of demo datasets are packaged with frePPLe. Using this action you can load one of those in the database.
The dataset is loaded incrementally in the database, without erasing any previous data. Normally you'll want to erase the data before loading any of these datasets.
The list of the datasets is dynamically constructed from the files in the folder freppledb/input/fixtures. Use the dumpdata command to export a model to the appropriate format and then copy your own additional datasets to this folder.
- Generate a model
For testing and benchmarking purposes it is extremely useful to be able to generate datasets with varying sizes and complexity. A few key parameters allow you to create a sample model for such test purposes.