# Framework overview

## Design 
The scheduler is designed stateless. Meaning no information of past solutions or problems
are saved. This brings the advantage, that a scheduler running as a mirco service
can be accessed by several clients, stopped, restarted and have its settings changed
without affecting running experiments using it.

## Algorithm plugin

By design, the scheduler provides to different scheduling algorithms.
There is a [solver_interface](../../src/labscheduler/solver_interface.py) defined
in python and every module placed in [this folder](../../src/labscheduler/solvers)
is automatically included.

### Choosing an algorithm

At any point in time the schedulers algorithm can be switched, e.g. via a SiLA command.
You can also request a list of available algorithms, request the basic information
on any available

[# TODO: release Insert exemplary ipython in-out correspondences]

### Shipped algorithms

Currently, there are the following scheduling algorithms implemented:

-   [Simple Solver](../solvers/simple.md) (More an academic example than actually usable)
-   [Basic Priority Dispatching Heuristic](../solvers/basic_pd_heuristics.md)
-   [Advanced Priority Dispatching Heuristic](../solvers/specialized_pd_heuristics.md) (recommended for quick solutions)
-   [MIP Optimizer](../solvers/mip_solver.md)
-   [CPOptimizer](../solvers/cp_solver.md) (recommended for best solutions)

A Graph Neural Network-Priority Dispatching Heuristic is currently under construction.