A Tutorial on how to Connect Python with Different Simulation Software to Develop Rich Simheuristics

Simulation is an excellent tool to study real-life systems with uncertainty. Discrete-event simulation (DES) is a common simulation approach to model time-dependent and complex systems. Therefore, there are a variety of commercial (Simio, AnyLogic, Simul8, Arena, etc.) and non-commercial (SimPy, Salabim, etc.) software packages that enable users to take advantage of DES modeling. Although these tools are capable of modeling real-life systems with a high accuracy, they generally fail to conduct advanced analytical analysis (i.e., machine learning, interactive visualizations) or complicated optimization (i.e., simheuristics). Therefore, coupling these DES platforms with external programming languages like Python offers additional mathematical operations and algorithmic flexibility. This integration makes the simulation modeling more intelligent and extends its applicability to a broader range of problems. This study aims to provide a step-wise tutorial for helping simulation users to create intelligent DES models by integrating them with Python. Multiple demo examples are discussed to provide insights and making this connection based on commercial and non-commercial DES packages.

1 Introduction and Related Work

Simulation packages are a perfect tool to model complex and dynamic systems with non-linear interactions. Several review papers and tutorials are written to enable users building simulation models using a simulation software or languages. Based on a subjective evaluation of parameters, Dias et al. (2016) made three clusters of simulation tools. The first cluster includes ProModel, FlexSim, Simul8, and WITNESS. The second cluster discusses ExtendSim, Simio, PlantSimulation, and AnyLogic. Finally, the third cluster covers SimProcess, AutoMod, Micro Saint, QUEST, Enterprise Dynamics, and Process Model. Arena appears in a different cluster. In another work, Schriber et al. (2012) provided several examples in AutoMod, SLX, ExtendSim, and Simio to guide user with simulation modeling. Besides, Dagkakis and Heavey (2016) reviewed an open-source DES with application in manufacturing, services, supply chain management, and logistics. In addition, authors evaluated and identified the best fitted simulation tools for different modeling needs. Later, Guimarães et al. (2018) developed a new method for selecting the right DES software to help and improve the efficiency of processes.