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# ProcessScheduler
A python library to compute resource-constrained task schedules. Express your scheduling problem in terms of tasks, resources and constraints, ProcessScheduler computes one/the best schedule that satisfies the requirements.
The computation is based on a set of constraints expressed under the form of first-order logic assertions. Problem solving is performed by the SAT/SMT [Z3 Theorem Prover](https://github.com/Z3Prover/z3).
## Documentation
User-end documentation available at https://processscheduler.readthedocs.io/
## Features
* tasks: zero duration task, fixed duration task, variable duration task, work amount, optional task,
* resources: worker, cumulative workers, workers selection, productivity attribute,
* advanced cost functions,
* buffer: NonConcurrentBuffer,
* task constraints: precedence, start synced, end synced, start at, end at, start after, end before,
* resource constraints: AllSameSelected, AllDifferentSelected,
* everything can be set as optional (tasks, resources, constraints),
* first-order-logic operations (not, or, xor, and, implies, if/then/else) between task or resource constraints,
* builtin and customized indicators (resource utilization, resource cost),
* single and multiobjective optimization (makespan, flowtime, earliest, latest, resource cost, etc.),
* exporters: smtlib2.0, json
* Gantt chart rendering using matplotlib or plotly
## Install
Install with pip.
```bash
pip install ProcessScheduler
```
The Z3 theorem prover is the only required dependency.
Optional dependencies (install either with pip or conda):
* matplotlib (Gantt chart rendering),
* plotly (Gantt chart rendering),
* numpy
## Try online
There are some Jupypter notebooks that can be executed online at [myBinder.org](https://mybinder.org/v2/gh/tpaviot/ProcessScheduler/HEAD?filepath=examples-notebooks)
## Helloworld
```python
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()
# display solution, ascii or matplotlib gantt diagram
solution.render_gantt_matplotlib()
```
## Code quality
ProcessScheduler uses the following tools to ensure code quality:
* unittests,
* code coverage (coverage.py, codecov.io),
* continuous-integration at MS azure,
* static code analysis (codacy),
* spelling mistakes tracking (codespell),
* code formatting using the black python formatter
## 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 (tpaviot@gmail.com).
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资源受限的进程调度程序__Python_下载.zip （120个子文件） 
Task.drawio 1KB TimeLineHorizon.drawio 1KB TwoTasksConstraint.drawio 85B .gitignore 437B .gitignore 7B MANIFEST.in 20B .mypy.ini 37B Excavator9.ipynb 73KB use-case-formula-one-change-tires.ipynb 13KB features.ipynb 9KB ui-modeling.ipynb 8KB use-case-flow-shop.ipynb 7KB use-case-software-development.ipynb 6KB ui-modeling.ipynb 6KB resource_constrained_project_scheduling.ipynb 4KB Excavator1.ipynb 3KB Excavator8.ipynb 3KB bike_shop.ipynb 3KB Excavator7.ipynb 3KB n_queens_job_shop.ipynb 3KB Excavator3.ipynb 2KB Excavator2.ipynb 2KB sports-scheduling.ipynb 2KB Excavator5.ipynb 2KB Excavator6.ipynb 2KB hello_world.ipynb 2KB Excavator4.ipynb 2KB small_hole.jpeg 14KB medium_hole.jpeg 13KB excavator_small.jpeg 12KB
excavator_medium_size.jpg 290KB huge_hole.jpg 218KB Makefile 634B README.md 4KB CONTRIBUTING.md 3KB NOTES 2KB
flow_shop_problem.png 32KB flow_shop_solution.png 29KB hello_world_gantt.png 14KB solver.py 27KB test_optional_task.py 22KB jupyter_ui.py 19KB task_constraint.py 17KB solution.py 16KB test_solver.py 16KB test_indicator.py 14KB problem.py 13KB test_task.py 13KB task.py 13KB resource_constraint.py 12KB test_resource_tasks_distance.py 11KB test_cost.py 11KB test_fol.py 9KB test_gantt.py 9KB json_io.py 9KB test_schedule_n_task_in_time_interval.py 8KB resource.py 8KB test_buffer.py 6KB excel_io.py 6KB test_workload.py 6KB test_cumulative.py 5KB context.py 5KB test_group_of_tasks.py 5KB test_features.py 5KB first_order_logic.py 4KB benchmark_mixed.py 4KB benchmark_logics.py 4KB benchmark_dev_team.py 4KB constraint.py 4KB benchmark_n_queens.py 4KB test_resource_unavailable.py 3KB test_io.py 3KB test_util.py 3KB util.py 3KB test_multiple_objectives.py 3KB objective.py 3KB base.py 3KB cost.py 3KB buffer.py 3KB conf.py 3KB test_datetime.py 3KB test_json_io.py 3KB test_dynamic_resource.py 3KB test_optional_constraint.py 2KB __init__.py 2KB setup.py 1KB test_jupyter_ui.py 1KB __main__.py 0B __init__.py 0B objectives.rst 12KB solving.rst 6KB task.rst 5KB task_constraints.rst 4KB resource.rst 4KB resource_constraints.rst 4KB scheduling_problem.rst 4KB first_order_logic_constraints.rst 3KB indicator.rst 3KB buffer.rst 3KB introduction.rst 3KB共 120 条
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