Optimisation
Find optimal variable settings for a target outcome.
Searches a digital twin for the lever settings that best hit a target, inside the constraints given.
Durable and long-running: it is many twin runs behind one node, so it is measured in twin evaluations rather than in a single call. results is the Pareto front it found, one row per plan: plan carries the run page's label (P1, P2, ...), recommended is true on the plan the run page badges Recommended, and each lever has a column with the setting that plan chooses. Filter results on recommended to get the Recommended plan.
At a glance
Module type
OptimisationSim
Category
Intelligence
Default tier
durable
Runs on
reactive, durable
Starts a flow
no
Holds a body
no
Keeps state between runs
no
Available in
every application type
Parameters
digitalTwinId
string · required · defaults to ""
The twin to run against.
digitalTwinName
string · optional
Its name when it was chosen, for the node's label.
digitalTwinVersionIds
string[] · optional
Pin the run to specific twin versions; empty means the latest.
scenario
Record<string, unknown> · optional
The saved scenario. Anything arriving on the scenario port is merged over this.
scenarioDraft
Record<string, unknown> · optional
What the inspector is editing, before it is applied to scenario.
scenarioValidationErrors
string[] · optional
What was wrong with the draft the last time it was checked.
entityColumns
object · optional · defaults to {"id":null,"target":null,"constraints":null,"levers":null}
Which columns of the entities frame carry what: id the environment id, and target, constraints and levers the optional per-row overrides. Any may be null.
Inputs
on
event
no
Implicit on every module that does not itself start a flow: wire an event here to make this module the start of what that event runs.
scenario
json
no
The objective, the levers to move and the bounds on them, per run.
entities
dataframe
no
One row per environment to optimise; entityColumns maps its columns.
baseline
dataframe
no
Rows to optimise from; unconnected, the twin's own baseline is used.
Outputs
results
dataframe
dataframe, signal
One row per Pareto solution: solution (its position in the front), plan (its run page label, such as P23), recommended (true on the Recommended plan), a column per objective, a column per lever holding the setting the plan chooses (suffixed _recommended when the name is taken), n_interventions, and the plan's notes. plan and recommended take a _plan suffix when an objective or lever already has that name.
summary
json
json, scalar, signal
The run's identity, not its findings: {runId, status, outputs}, where outputs is the simulator's raw per-version result. The numbers a reader wants are in results.
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