relationalai.experimental.solvers.if_then_else()
if_then_else( cond: dsl.Instance|SolverExpression, left: dsl.Instance|SolverExpression, right: dsl.Instance|SolverExpression) -> SolverExpression
Returns a solver expression that evaluates to left
when the binary condition cond
is true, and to right
when cond
is false.
cond
must be a binary-valued solver expression ( True
/ False
or 1
/ 0
).
Must be called in a Model.rule()
or solvers.operators()
context.
Parameters
Section titled “Parameters”Name | Type | Description |
---|---|---|
cond | dsl.Instance , SolverExpression | A binary condition controlling which branch is selected. |
left | dsl.Instance , SolverExpression | The expression returned when cond is true. |
right | dsl.Instance , SolverExpression | The expression returned when cond is false. |
Returns
Section titled “Returns”A SolverExpression
object.
Example
Section titled “Example”Use if_then_else()
with the SolverModel.constraint()
method to ensure an employee is assigned exactly one shift per day:
import relationalai as raifrom relationalai.experimental import solvers
# Create a RAI model.model = rai.Model("WeeklyShiftAssignment")
# Declare entity types and properties.
# Entity types.Employee = model.Type("Employee")Shift = model.Type("Shift")Day = model.Type("Day")Available = model.Type("Available")Scenario = model.Type("Scenario") # All possible employee–shift–day combinations.Assignment = model.Type("Assignment") # A Scenario that is assigned.
# Properties.Employee.name.declare()Shift.name.declare()Shift.capacity.declare()Available.employee.declare()Available.day.declare()Scenario.employee.declare()Scenario.shift.declare()Scenario.day.declare()
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# Define sample data.
# Employees.with model.rule(dynamic=True): for name in ["Alice", "Bob", "Carol", "Dave", "Eve"]: Employee.add(name=name)
# Shifts.with model.rule(): Shift.add(name="Morning").set(capacity=2) Shift.add(name="Evening").set(capacity=3)
# Days of the week.with model.rule(dynamic=True): for name in ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]: Day.add(name=name)
# Employee-day availability.with model.rule(dynamic=True): # Alice works weekdays only for name in ["Mon", "Tue", "Wed", "Thu", "Fri"]: Available.add(employee=Employee(name="Alice"), day=Day(name=name)) # Bob works all days Available.add(employee=Employee(name="Bob"), day=Day()) # Carol works weekends only for name in ["Sat", "Sun"]: Available.add(employee=Employee(name="Carol"), day=Day(name=name)) # Dave works Mon/Wed/Fri for name in ["Mon", "Wed", "Fri"]: Available.add(employee=Employee(name="Dave"), day=Day(name=name)) # Eve works Tue/Thu/Sat for name in ["Tue", "Thu", "Sat"]: Available.add(employee=Employee(name="Eve"), day=Day(name=name))
# All possible employee–shift–day combinationswith model.rule(): Scenario.add(employee=Employee(), shift=Shift(), day=Day())
# Create a SolverModel instance from the model.solver_model = solvers.SolverModel(model)
# Define solver variables.
# Scenario assignment: binary variable for each employee–shift–day combination.with model.rule(): scenario = Scenario() solver_model.variable( scenario, type="zero_one", name_args=["assigned", scenario.employee.name, scenario.day.name, scenario.shift.name], )
# Define solver constraints.
# Alice must have exactly one shift each day she is available.with solvers.operators(): alice = Employee(name="Alice") day = Available(employee=alice).day works_morning = Scenario(employee=alice, shift=Shift(name="Morning"), day=day) works_evening = Scenario(employee=alice, shift=Shift(name="Evening"), day=day) solver_model.constraint( solvers.if_then_else(works_morning, solvers.not_(works_evening), works_evening), name_args=["alice_shift", day.name] )