OptaPy
OptaPy is an AI constraint solver for Python to optimize the Vehicle Routing Problem, Employee Rostering, Maintenance Scheduling, Task Assignment, School Timetabling, Cloud Optimization, Conference Scheduling, Job Shop Scheduling, Bin Packing and many more planning problems.
OptaPy wraps the OptaPlanner engine internally, but using OptaPy in Python is significantly slower than using OptaPlanner in Java or Kotlin.
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Warning
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OptaPy is an experimental technology. It is at least 20 times slower than using OptaPlanner in Java or Kotlin. It is currently not supported in any way or form for production use. |
Get started
Prerequisites
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Install JDK 11 or later with
JAVA_HOMEconfigured appropriately.
Build
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Install the python
buildmodule (if not already installed):pip install build
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In the
optapy-coredirectory, use the command below to build the optapy python wheel into thedistdirectory:cd optapy-core python -m build -
Install it into a virtual environment using
pip:# Activate a virtual environment first source my_project/venv/bin/activate pip install dist/optapy-0.0.0-py3-none-any.whl
Run
Running run.sh runs optapy-quickstarts/school-timetabling/main.py after building optapy and installing it to optapy-quickstarts/school-timetabling/venv.
Source code overview
Domain
In OptaPlanner, the domain has three parts:
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Problem Facts, which do not change
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Planning Entities, which have one or more planning variables
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Planning Solution, which define the facts and entities of the problem
Problem Facts
To declare Problem Facts, use the @problem_fact decorator
from optapy import problem_fact
@problem_fact
class Timeslot:
def __init__(self, id, dayOfWeek, startTime, endTime):
self.id = id
self.dayOfWeek = dayOfWeek
self.startTime = startTime
self.endTime = endTime
Planning Entities
To declare Planning Entities, use the @planning_entity decorator
from optapy import planning_entity, planning_id, planning_variable
@planning_entity
class Lesson:
def __init__(self, id, subject, teacher, studentGroup, timeslot=None, room=None):
self.id = id
self.subject = subject
self.teacher = teacher
self.studentGroup = studentGroup
self.timeslot = timeslot
self.room = room
@planning_id
def getId(self):
return self.id
@planning_variable(Timeslot, value_range_provider_refs=["timeslotRange"])
def getTimeslot(self):
return self.timeslot
def setTimeslot(self, newTimeslot):
self.timeslot = newTimeslot
@planning_variable(Room, value_range_provider_refs=["roomRange"])
def getRoom(self):
return self.room
def setRoom(self, newRoom):
self.room = newRoom
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@planning_variablemethod decorators are used to indicate what fields can change. The method MUST follow JavaBean style conventions and have a corresponding setter (i.e.getRoom(self),setRoom(self, newRoom)). The first parameter of the decorator is the type of the Planning Variable (required). Thevalue_range_provider_refsparameter tells OptaPlanner what value range providers on the Planning Solution this Planning Variable can take values from. -
@planning_idis used to uniquely identify an entity object of a particular class. The same Planning Id can be used on entities of different classes, but the ids of all entities in the same class must be different.
Planning Solution
To declare the Planning Solution, use the @planning_solution decorator
from optapy import planning_solution, problem_fact_collection_property, value_range_provider, planning_entity_collection_property, planning_score
@planning_solution
class TimeTable:
def __init__(self, timeslotList=[], roomList=[], lessonList=[], score=None):
self.timeslotList = timeslotList
self.roomList = roomList
self.lessonList = lessonList
self.score = score
@problem_fact_collection_property(Timeslot)
@value_range_provider(range_id = "timeslotRange")
def getTimeslotList(self):
return self.timeslotList
@problem_fact_collection_property(Room)
@value_range_provider(range_id = "roomRange")
def getRoomList(self):
return self.roomList
@planning_entity_collection_property(Lesson)
def getLessonList(self):
return self.lessonList
@planning_score(HardSoftScore)
def getScore(self):
return self.score
def setScore(self, score):
self.score = score
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@value_range_provider(range_id)is used to indicate a method returns values a Planning Variable can take. It can be referenced by its id in thevalue_range_provider_refsparameter of@planning_variable. It should also have a@problem_fact_collection_propertyor a@planning_entity_collection_property. -
@problem_fact_collection_property(type)is used to indicate a method returns Problem Facts. The first parameter of the decorator is the type of the Problem Fact Collection (required). It should be a list. -
@planning_entity_collection_property(type)is used to indicate a method returns Planning Entities. The first parameter of the decorator is the type of the Planning Entity Collection (required). It should be a list. -
@planning_score(scoreType)is used to tell OptaPlanner what field holds the score. The method MUST follow JavaBean style conventions and have a corresponding setter (i.e.getScore(self),setScore(self, score)). The first parameter of the decorator is the score type (required).
Constraints
You define your constraints by using the ConstraintFactory
import java
from domain import Lesson
from optapy import get_class, constraint_provider
from optapy.types import Joiners, HardSoftScore
# Get the Java class corresponding to the Lesson Python class
LessonClass = get_class(Lesson)
@constraint_provider
def defineConstraints(constraintFactory):
return [
# Hard constraints
roomConflict(constraintFactory),
# Other constraints here...
]
def roomConflict(constraintFactory):
# A room can accommodate at most one lesson at the same time.
return constraintFactory \
.fromUniquePair(LessonClass, [
# ... in the same timeslot ...
Joiners.equal(lambda lesson: lesson.timeslot),
# ... in the same room ...
Joiners.equal(lambda lesson: lesson.room)]) \
.penalize("Room conflict", HardSoftScore.ONE_HARD)
for more details on Constraint Streams, see https://docs.optaplanner.org/latest/optaplanner-docs/html_single/index.html#constraintStreams
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Note
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Since from is a keyword in python, to use the constraintFactory.from function, you access it like constraintFactory.from_(class, [joiners…]) |
Solve
from optapy import get_class, solve
from optapy.types import SolverConfig, Duration
from constraints import defineConstraints
from domain import TimeTable, Lesson, generateProblem
import java
solverConfig = SolverConfig().withEntityClasses(get_class(Lesson)) \
.withSolutionClass(get_class(TimeTable)) \
.withConstraintProviderClass(get_class(defineConstraints)) \
.withTerminationSpentLimit(Duration.ofSeconds(30))
solution = solve(solverConfig, generateProblem())
solution will be a TimeTable instance with planning variables set to the final best solution found.
More information
Visit www.optaplanner.org.
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