Ciw 1.1.3

A discrete event simulation library for queueing networks

Ciw is a discrete event simulation library for open queueing networks. It’s core features include the capability to simulate networks of queues, multiple customer classes, and implementation of Type I blocking for restricted networks. A number of other features are also implemented, including priorities, baulking, schedules, and deadlock detection.

• Read the documentation
• Example Jupyter Notebooks
• Contribution guidelines
• Our great contributors
• Install with pip install ciw

Usage

Import Ciw:

>>> import ciw

To define an M/M/3 queue, with λ = 0.2 and μ = 0.1:

>>> N = ciw.create_network(
...     Arrival_distributions=[['Exponential', 0.2]],
...     Service_distributions=[['Exponential', 0.1]],
...     Number_of_servers=[3]
... )

Now set a seed, create a Simulation object, and simulate for 1440 time units:

>>> ciw.seed(1)
>>> Q = ciw.Simulation(N)
>>> Q.simulate_until_max_time(1440)

Collect results:

>>> recs = Q.get_all_records()

Manipulate results to get useful statistics, e.g. average waiting time:

>>> waits = [r.waiting_time for r in recs]
>>> sum(waits) / len(waits)
1.6885...

History

v1.1.3 (2017-08-18)

• Replace DataRecord object with namedtuple.
• Number of minor tweaks for speed improvements.

v1.1.2 (2017-07-05)

• Batch arrivals.

v1.1.1 (2017-06-23)

• Server utilisation & overtime.
• Small fixes to docs.
• Testing on Python 3.6.

v1.1.0 (2017-04-26)

• Replace kwargs with actual keyword arguments in ciw.create_network.
• Refactor server schedule inputs (schedules placed inside Number_of_servers instead of as their own keyword).

v1.0.0 (2017-04-04)

• ciw.create_network takes in kwargs, not dictionary.
• Add Sequential distribution.
• Add truncated Normal distribution.
• Refactor inputs for custom PDF.
• Refactor inputs for server schedules.
• Transition matrix now optional for 1 node networks.
• Overhaul of documentation.
• Add CONTRIBUTING.rst.
• Slight improvement of ciw.random_choice.

v0.2.11 (2017-03-13)

• Add ability to simulate until max number of customers have passed arrived/been accepted/passed through the system.

v0.2.10 (2017-03-10)

• Performance improvements.
• Drop dependency on numpy.

v0.2.9 (2017-02-24)

• Allow zero servers.

v0.2.8 (2016-11-10)

• Add option for time dependent distributions.

v0.2.7 (2016-10-26)

• Run tests on Appveyor.
• Check docs build and pip installable on Travis.
• Remove hypothesis cache.

v0.2.6 (2016-10-17)

• Add AUTHORS.rst.
• Add progress bar option.

v0.2.5 (2016-10-06)

• Fix bug that didn’t include .rst files in MANIFEST.in.

v0.2.4 (2016-09-27)

• Fixed bug in which priority classes and dynamic classes didn’t work together.
• New feature: preemptive interruptions for server schedules.

v0.2.3 (2016-07-27)

• Ability to set seed. More docs. Fixes to tests.

v0.2.2 (2016-07-06)

• Baulking implemented, and minor fixes to order of unblocking.

v0.2.1 (2016-06-29)

• Priority classes implemented.

v0.2.0 (2016-06-20)

• Python 3.4 and 3.5 compatible along with 2.7.
• Data records now kept in list.

v0.1.1 (2016-06-06)

• Ability to incorporate behaviour nodes.
• Data records are now named tuples.

v0.1.0 (2016-04-25)

• Re-factor inputs.
• Simulation takes in a Network object.
• Helper functions to import yml and dictionary to a Network object.
• Simulation object takes optional arguments: deadlock_detector, exact, tracker.
• simulate_until_max_time() takes argument max_simulation_time.

v0.0.6 (2016-04-04)

• Exactness implemented.
• Restructure some features e.g. times_to_deadlock.
• Custom simulation names.

v0.0.5 (2016-03-18)

• State space tracker plug-and-playable.
• Add rejection dictionary.

v0.0.4 (2016-02-20)

• Empirical and UserDefined distributions added.
• Tidy ups.

v0.0.3 (2016-02-09)

• Arrival distributions.
• MMC options removed.
• Fix server schedule bugs.

v0.0.2 (2016-01-06)

• Some kwargs optional.
• Hypothesis tests.
• Minor enhancements.

v0.0.1 (2015-12-14)

• Initial release.

v0.0.1dev (2015-12-14)

• Initial release (dev).

Contributors

Ciw has been developed by the following contributors:

• Geraint Palmer
• Vincent Knight
• Lieke Hölscher
• Sam Luen-English
• Alex Carney
• Adam Johnson
• Nikoleta Glynatsi