PythonLab Scheduler

Generic Scheduler for Lab Automation Processes. 'Scheduling' in this sense means taking a workflow description and a descriptions of a lab(also called job shop) and assigning timestamps and machines to the steps of the workflow in an intelligent way.

flowchart TD
A[Scheduler]--computes-->B[Schedule];
B --contains-->C[Start Times];
B --contains-->D[Machine Assignments];
E[Job Shop]--YAML-->A;
F[Workflow]--python-->A;
G[Precedences]--make up-->F;
H[Process Steps]--make up-->F;
I[Machines]--make up-->E;

In other words: It solves a generalization of the Job Shop Scheduling Problem(JSSP). The main generalizations are

• Jobs can have several operations on the same machine
• There might be several machines of a kind
• Time-constraints between Operations (soft and hard)
• Machines can have capacities to do several operations in parallel
• Jobs can intersect(i.e. operations can have precedence constraints to other jobs operations)

More precise information on this can be found here

Installation

pip install labscheduler --index-url https://gitlab.com/api/v4/projects/25166382/packages/pypi/simple

Scheduling Framework Overview

The framework is designed to be very generic: The core scheduling algorithm can be exchanged by any scheduling concept that shall be developed, tested or used in a real world application. The scheduler itself can be used via python import or as a micro service via SiLA. More information in the framework can be found here.

graph TD;
  A[User] <-- python --> B[Scheduler];
  A <-- sila_python --> C[SiLA-Client];
  C <-- SiLA --> D[SiLA-Server];
  B <--> D;
  B --> E[AlgorithmInterface];
  E -- implemented by --> F[Algorithm];
  E -- implemented by --> G[Algorithm];
  E -- implemented by --> H[Algorithm];

Usage

For help in the console type

labscheduler --help

The scheduler can be accessed via standard python import or run on a SiLA-server and accessed via a SiLA-client. Examples can be found here:

Development

git clone gitlab.com/opensourcelab/labscheduler

# create a virtual environment and activate it then run

pip install -e .[dev]

# run unittests

invoke test   # use the invoke environment to manage development

# commit changes: please install pre-commit hooks
pre-commit install

Documentation

The Documentation can be found here: https://opensourcelab.gitlab.io/labscheduler

Credits

This package was created with Cookiecutter and the gitlab.com/opensourcelab/software-dev/cookiecutter-pypackage project template.