ProcessScheduler 0.4.0

A package to solve resource-constrained scheduling problems using SMT theory/solver.

ProcessScheduler

A python library to compute resource-constrained task schedules. Documentation at https://processscheduler.readthedocs.io/

pip install ProcessScheduler

About

The computation is based on a set of constraints expressed under the form of first-order logic assertions. Problem solving is performed by the SMT Z3Prover. This project was inspired by the work from Tim Nonner at https://github.com/timnon/pyschedule.

import processscheduler as ps
# a simple problem, without horizon (solver will find it)
pb = ps.SchedulingProblem('HelloWorldProcessScheduler')

# add two tasks
task_hello = ps.FixedDurationTask('Process', duration=2)
task_world = ps.FixedDurationTask('Scheduler', duration=2)

# precedence constraint: task_world must be scheduled
# after task_hello
c1 = ps.TaskPrecedence(task_hello, task_world, offset=0)
pb.add_constraint(c1) # explicitly add this constraint to the problem

# solve
solver = ps.SchedulingSolver(pb)
solution = solver.solve()

# displays solution, ascii or matplotlib gantt diagram
solution.render_gantt_matplotlib()

Features

• tasks: zero duration task, fixed duration task, variable duration task, work amount, optional task,
• resources: worker, cumulative workers, workers selection, cost_per_period and productivity attributes,
• task constraints: precedence, start synced, end synced, start at, end at, start after, end before,
• optional tasks,
• optional task constraints: task schedule condition, tasks schedule dependencies,
• resource constraints: AllSameSelected, AllDifferentSelected,
• optional resource constraints,
• first-order-logic operations (not, or, xor, and, implies, if/then/else) between task or resource constraints,
• builtin indicators (resource utilization, resource cost),
• customized indicators,
• SAT/SMT solver with or without optimization,
• objective (makespan, flowtime, earliest, latest, resource cost).

Jupyter notebooks

There are some Jupypter notebooks. They can be executed online at myBinder.org

Code quality

ProcessScheduler uses the following tools/methods to ensure code quality:

• unittests,
• code coverage (coverage.py, codecov.io),
• continuous-integration at MS azure,
• static code analysis (codacy),
• spelling mistakes tracking (codespell)

License/Author

ProcessScheduler is distributed under the terms of the GNU General Public License v3 or (at your option) any later version. It is currently developed and maintained by Thomas Paviot ([e-mail]).