Design of an Extensible BPMN Process Simulator

Pufahl, Luise; Wong, Tsun Yin; Weske, Mathias

doi:10.1007/978-3-319-74030-0_62

Cited by 22 publications

(24 citation statements)

References 8 publications

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“…Following Jansen-Vullers and Netjes ( 2006), Bosilj -Vuks ˇic ´et al ( 2007) and Pufahl et al (2018), we differentiate three different types of simulation tools for our analysis: (a) Business process modeling or management tools with simulation support (e.g., ADONIS, ARIS Toolset, Bizagi Modeler); (b) General purpose simulation tools (e.g., Arena, AnyLogic); and (c) Stand-alone business process simulators (e.g., Bimp).…”

Section: Extension Approaches Do Not Rely On Transformations Tomentioning

confidence: 95%

“…Moreover, idiosyncratic extensions to process modeling languages have been proposed, typically in combination with proprietary simulation tool support (cf. Gawin and Marcinkowski 2015;Laue and Mu ¨ller 2016;Pufahl et al 2018).…”

Section: Extension Approaches Do Not Rely On Transformations Tomentioning

confidence: 99%

“…Extension approaches use simulation tools implementing the extensions needed for simulation to run the corresponding process simulation. Idiosyncratic extension approaches to process modeling languages are typically accompanied by proprietary simulation tool support (Laue and Mu ¨ller 2016;Pufahl et al 2018).…”

Section: Extension Approaches Do Not Rely On Transformations Tomentioning

confidence: 99%

See 2 more Smart Citations

Business Process Simulation on Procedural Graphical Process Models

2021

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Business process simulation marks an essential technique for analyzing business processes and for reasoning about process improvement. With first contributions dating back to the mid-1990s, computerized business process simulation has been a continuing research focus and is widely acknowledged as foundational to Business Process Management research and practice. Reviewing contributions to the field published between 1990 and 2018, the authors assess the state of research on business process simulation and develop an organizing overview of research contributions discussing simulation approaches, tool support, results visualization, use context, application purposes, and adoption barriers. Findings inform future research on business process simulation by discussing paths for behavioral research on the use of business process simulation, user requirements, and adoption barriers as well as complementary paths for design science research addressing limitations of present approaches and simulation tool support.

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Section: Extension Approaches Do Not Rely On Transformations Tomentioning

confidence: 95%

Section: Extension Approaches Do Not Rely On Transformations Tomentioning

confidence: 99%

Section: Extension Approaches Do Not Rely On Transformations Tomentioning

confidence: 99%

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Business Process Simulation on Procedural Graphical Process Models

2021

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“…The algorithm in [20] identifies 5 resource pools and assigned each activity to exactly one pool. 6 .…”

Section: Pre-processingmentioning

confidence: 99%

“…Below we discuss how the accuracy of the resulting BPS model is assessed and optimized. 6 It turns out that this event log contains information about roles and about the mapping from activities to roles. We found that the algorithm in [20] re-discovered the roles already present in the event log (without using this information) with 100% accuracy…”

Section: Pre-processingmentioning

confidence: 99%

Automated discovery of business process simulation models from event logs

Camargo

Dumas

González-Rojas

2020

Decision Support Systems

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Simulation is a versatile technique for quantitative analysis of business processes. It allows analysts to estimate the performance of a process under multiple scenarios. However, the discovery, validation, and tuning of business process simulation models is cumbersome and error-prone. It requires manual iterative refinement of the process model and simulation parameters in order to match the observed reality as closely as possible. Modern information systems store detailed execution logs of the business processes they support. Previous work has shown that these logs can be used to discover simulation models. However, existing methods for log-based discovery of simulation models do not seek to optimize the accuracy of the resulting models. Instead they leave it to the user to manually tune the simulation model to achieve the desired level of accuracy. This article presents an accuracy optimized method to discover business process simulation models from execution logs. The method decomposes the problem into a series of steps with associated configuration parameters. A hyper-parameter optimization method is then used to search through the space of possible configurations so as to maximize the similarity between the behavior of the simulation model and the behavior observed in the log. The method has been implemented as a tool and evaluated using logs from different domains.

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