OptaPy Introduction
1. What is OptaPy
Every organization faces planning problems: providing products or services with a limited set of constrained resources (employees, assets, time and money). OptaPy optimizes such planning to do more business with less resources. This is known as Constraint Satisfaction Programming (which is part of the Operations Research discipline).
OptaPy is a wrapper of the OptaPlanner constraint satisfaction engine which optimizes planning problems. It solves use cases such as:

Employee shift rostering: timetabling nurses, repairmen, …

Agenda scheduling: scheduling meetings, appointments, maintenance jobs, advertisements, …

Educational timetabling: scheduling lessons, courses, exams, conference presentations, …

Vehicle routing: planning vehicle routes (trucks, trains, boats, airplanes, …) for moving freight and/or passengers through multiple destinations using known mapping tools …

Bin packing: filling containers, trucks, ships, and storage warehouses with items, but also packing information across computer resources, as in cloud computing …

Job shop scheduling: planning car assembly lines, machine queue planning, workforce task planning, …

Cutting stock: minimizing waste while cutting paper, steel, carpet, …

Sport scheduling: planning games and training schedules for football leagues, baseball leagues, …

Financial optimization: investment portfolio optimization, risk spreading, …
2. What is a planning problem?
A planning problem has an optimal goal, based on limited resources and under specific constraints. Optimal goals can be any number of things, such as:

Maximized profits  the optimal goal results in the highest possible profit.

Minimized ecological footprint  the optimal goal has the least amount of environmental impact.

Maximized satisfaction for employees or customers  the optimal goal prioritizes the needs of employees or customers.
The ability to achieve these goals relies on the number of resources available, such as:

The number of people.

Amount of time.

Budget.

Physical assets, for example, machinery, vehicles, computers, buildings, etc.
Specific constraints related to these resources must also be taken into account, such as the number of hours a person works, their ability to use certain machines, or compatibility between pieces of equipment.
OptaPy helps Python^{TM} programmers solve constraint satisfaction problems efficiently. Under the hood, it combines optimization heuristics and metaheuristics with very efficient score calculation.
2.1. A planning problem is NPcomplete or NPhard
All the use cases above are probably NPcomplete/NPhard, which means in layman’s terms:

It’s easy to verify a given solution to a problem in reasonable time.

There is no silver bullet to find the optimal solution of a problem in reasonable time (*).
(*) At least, none of the smartest computer scientists in the world have found such a silver bullet yet. But if they find one for 1 NPcomplete problem, it will work for every NPcomplete problem. In fact, there’s a $ 1,000,000 reward for anyone that proves if such a silver bullet actually exists or not. 
The implication of this is pretty dire: solving your problem is probably harder than you anticipated, because the two common techniques won’t suffice:

A Brute Force algorithm (even a smarter variant) will take too long.

A quick algorithm, for example in bin packing, putting in the largest items first, will return a solution that is far from optimal.
By using advanced optimization algorithms, OptaPy does find a nearoptimal solution in reasonable time for such planning problems.
2.2. A planning problem has (hard and soft) constraints
Usually, a planning problem has at least two levels of constraints:

A (negative) hard constraint must not be broken. For example: 1 teacher cannot teach 2 different lessons at the same time.

A (negative) soft constraint should not be broken if it can be avoided. For example: Teacher A does not like to teach on Friday afternoon.
Some problems have positive constraints too:

A positive soft constraint (or reward) should be fulfilled if possible. For example: Teacher B likes to teach on Monday morning.
Some basic problems (such as N queens]) only have hard constraints. Some problems have three or more levels of constraints, for example hard, medium and soft constraints.
These constraints define the score calculation (AKA fitness function) of a planning problem. Each solution of a planning problem can be graded with a score. With OptaPy, score constraints are written in an Object Oriented language, such as Python^{TM} code. Such code is easy, flexible and scalable.
2.3. A planning problem has a huge search space
A planning problem has a number of solutions. There are several categories of solutions:

A possible solution is any solution, whether or not it breaks any number of constraints. Planning problems tend to have an incredibly large number of possible solutions. Many of those solutions are worthless.

A feasible solution is a solution that does not break any (negative) hard constraints. The number of feasible solutions tends to be relative to the number of possible solutions. Sometimes there are no feasible solutions. Every feasible solution is a possible solution.

An optimal solution is a solution with the highest score. Planning problems tend to have 1 or a few optimal solutions. There is always at least 1 optimal solution, even in the case that there are no feasible solutions and the optimal solution isn’t feasible.

The best solution found is the solution with the highest score found by an implementation in a given amount of time. The best solution found is likely to be feasible and, given enough time, it’s an optimal solution.
Counterintuitively, the number of possible solutions is huge (if calculated correctly), even with a small dataset. As you can see in the examples, most instances have a lot more possible solutions than the minimal number of atoms in the known universe (10^80). Because there is no silver bullet to find the optimal solution, any implementation is forced to evaluate at least a subset of all those possible solutions.
OptaPy supports several optimization algorithms to efficiently wade through that incredibly large number of possible solutions. Depending on the use case, some optimization algorithms perform better than others, but it’s impossible to tell in advance. With OptaPy, it is easy to switch the optimization algorithm, by changing the solver configuration in a few lines of code.
3. Requirements
OptaPy is open source software, released under the Apache License 2.0. This license is very liberal and allows reuse for commercial purposes. Read the layman’s explanation.
OptaPy is a solver for Python that uses a Java Virtual Machine underneath. To use OptaPy, install both Python 3.9 or later and Java Development Kit (JDK) 11 or later. OptaPy is available in the Python Package Index.
OptaPy works with CPython.
4. Governance
4.1. Status of OptaPy
OptaPy is currently an alpha release under active development. Breaking changes may happen between releases while in alpha.
4.2. Release notes
OptaPlanner releases every month. Since OptaPy is a wrapper for OptaPlanner, it shares its release notes. Read the release notes of each release on our website.
4.3. Backwards compatibility
OptaPy is currently an alpha release under active development. Breaking changes may happen between releases while in alpha.
4.4. Community and support
For news and articles, check our blog,
twitter (including Geoffrey’s twitter)
and facebook.
If you’re happy with OptaPy, make us happy by posting a tweet or blog article about it.
Public questions are welcome on here. Bugs and feature requests are welcome in our issue tracker. Pull requests are very welcome on GitHub and get priority treatment! By open sourcing your improvements, you’ll benefit from our peer review and from our improvements made on top of your improvements.
5. Use OptaPy with pip
OptaPy is available in the Python Package Index (PyPI) as the optapy
project. To install, run the following command:
pip install optapy
5.1. Build OptaPy from source
Prerequisites

Set up Git.

Authenticate on GitHub using either HTTPS or SSH.

See GitHub for more information about setting up and authenticating Git.


Install the python build module
Build and run the examples from source.

Clone
optapy
from GitHub (or alternatively, download the zipball):$ git clone https://github.com/optapy/optapy.git ...

Build it with Python:
$ cd optapycore $ python m build ...
The first time, the build might take a long time, because it needs to download jars.

Install the built package to a virtual environment
$ cp dist/optapy*py3noneany.whl path/to/my/application $ cd path/to/my/application $ source myapplicationvenv/bin/activate $ pip install optapy*py3noneany.whl

Edit the sources in your favorite IDE.