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.

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Install with pip install ciw.

Current supported version of Python:

• Python 3.8
• Python 3.9
• Python 3.10
• Python 3.11
• Python 3.12

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=[ciw.dists.Exponential(rate=0.2)], ... service_distributions=[ciw.dists.Exponential(rate=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) 4.2305... 

A number of other features are also implemented, including:

• Type I blocking
• A large range of sampling distributions
• Phase-Type distributions
• Time-dependent and state-dependent distributions
• Batch arrivals
• Baulking customers
• Reneging customers
• Processor sharing
• Multiple customer classes
• Priorities
• Server priorities
• Service disciplines
• Customers changing classes while queueing
• Customers changing classes after service
• Server schedules
• Slotted services
• State tracking
• Stopping the simulation after a certain amount of customers
• Process-based routing
• Logical routing
• Deadlock detection