The end goal of Problem Modeling is to produce a problem description to feed the Optimization Engine.
As soon as possible, graphical models produced with this tool are refined to instantiate an actual multi-agent system. This is done through high-level descriptions that are almost textual translations of the graphical models, except that they allow for more details.
In practice, this description is a groovy map in the Run Logic. Then a specific API transforms this map into JSON. Then the backend transforms the JSON problem description into an actual multi-agent system.
In this section, all the code snippets are in YAML, since it is basically JSON but readable by a human.
The steps to create a specific model description are:
Run.groovyLogic: understand its structureDefine the dimensions hierarchies
Define the spaces
Define the scopes inside each space
Define the variables inside each scope
Define the criteria inside each scope
The easiest way is to write the description space by space following the graphical description. The binding to the data is done through scopes: inputs and outputs of the Optimization Engine are model tables named after the space and scopes to which they pertain (e.g. Problem_ByProduct_All contains the data for the scope All in the space ByProduct).
Understanding the underlying concepts is important. Page Main Concepts for Optimization Problems is the reference.
This section also provides a problem description sample.
Further in this section:
A problem description does not exist in isolation and is delivered along with a Model Logic responsible for providing the mappings, in the form of Model Tables, between this high-level description of the problem and the actual values coming from the partition’s transactional and master data. This step is documented in the next section Problem Tables.