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Chapter 4Simulation Programmingwith PythonThis chapter shows how simulations of some of the examples in Chap. 3 can beprogrammed using Python and the SimPy Simulation library[1]. The goals ofthe chapter are to introduce SimPy, and to hint at the experiment design andanalysis issues that will be covered in later chapters. While this chapter willgenerally follow the flow of Chap. 3, it will use the conventions and patternsenabled by the SimPy SimPy OverviewSimPy is an object-oriented, process-based discrete-event Simulation library forPython. It is open source and released under the M license. SimPy providesthe modeler with components of a Simulation model including processes, foractive components like customers, messages, and vehicles, and resources, forpassive components that form limited capacity congestion points like servers,checkout counters, and tunnels. It also provides monitor variables to aid ingathering statistics. Random variates are provided by the standard Pythonrandom module.

4.2 Simulating the M(t)=M=1Queue Here we consider the parking lot example of Sect. 3.1a queueing system with time-varying car arrival rate, exponentially distributed parking time and an in nite number of parking spaces. A SimPy simulation generally consists of import of modules, de ning pro-

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