List of discrete event simulation software

Discrete event simulation (DES) software comprises specialized computer programs that model the operation of complex, dynamic systems as a sequence of discrete events occurring at specific points in time, where each event instantaneously alters the system's state variables, such as resource availability or queue lengths.^[1] These tools simulate stochastic processes by advancing a virtual clock only to the next event time, using mechanisms like future event lists to manage event scheduling and track system performance metrics, including throughput, utilization, and wait times.^[2] DES software is essential for analyzing systems with inherent uncertainty and time-varying behaviors, enabling users to test "what-if" scenarios without disrupting real-world operations.^[3] Key applications span industries such as manufacturing (e.g., production line optimization), logistics and supply chain (e.g., inventory and transportation modeling), healthcare (e.g., patient flow and resource allocation), and communications networks (e.g., traffic and data handling).^[4][5][6] By incorporating probabilistic elements like random arrival times or failure rates, these simulations provide insights into system efficiency, bottlenecks, and risk factors under various conditions.^[6] The landscape of DES software includes both commercial and open-source offerings, each catering to different user needs in terms of ease of use, scalability, and cost. Commercial packages, such as Arena (developed by Rockwell Automation for process analysis and design) and AnyLogic (supporting multimethod modeling including DES), offer graphical interfaces, extensive libraries, and professional support for enterprise-level applications.^[6][4] In contrast, open-source alternatives like SimPy (a Python-based library for building custom simulations) and JaamSim (featuring drag-and-drop interfaces for free modeling) provide flexibility for academic, research, or budget-constrained environments, often emphasizing programmability and extensibility.^[7][8] This diversity allows practitioners to select tools based on project complexity, from simple queueing models to large-scale hybrid simulations integrating DES with agent-based or continuous approaches.^[9]

Fundamentals of Discrete Event Simulation

Core Concepts and Principles

Discrete event simulation (DES) is a modeling approach used to represent the operation of a system as a sequence of events that occur at distinct points in time, where the state of the system changes only when an event takes place, such as an arrival, departure, or failure.^[10] This paradigm is particularly suited for systems involving queues, resources, and stochastic processes, where time progresses in variable increments rather than fixed steps.^[2] At the heart of DES are several key principles that govern its execution. Event scheduling involves maintaining a future event list (FEL), an ordered queue of upcoming events sorted by their scheduled times, which drives the simulation forward.^[10] Time advancement employs the next-event time advance mechanism, where the simulation clock jumps directly to the timestamp of the earliest event on the FEL, executes it to update the system state, schedules any resulting future events, and collects relevant statistics before proceeding.^[11] Entity flow tracks dynamic objects, such as customers or parts, as they navigate through the system, interacting with resources like servers or machines that must be allocated and released via event-driven changes.^[10] Resource management ensures these elements are modeled realistically, accounting for availability and contention, often informed by queueing theory fundamentals; for instance, Little's Law states that the average number of entities in the system (L) equals the arrival rate (λ) times the average time spent in the system (W), providing a foundational relationship for analyzing steady-state performance. DES differs from continuous simulation, which models systems through differential equations to capture smooth, ongoing changes in state variables over time, such as fluid flows or population growth, whereas DES focuses on abrupt, event-triggered transitions suitable for discrete interactions.^[2] In contrast to agent-based modeling, which emphasizes autonomous agents making individual decisions and evolving through interactions in a bottom-up manner, DES relies on centralized event queues to orchestrate predefined processes and entity movements.^[12] Essential components of DES software include the event list for managing scheduled occurrences, random number generators to introduce stochasticity in event timings and outcomes via probability distributions, and mechanisms for computing output statistics such as throughput (events processed per unit time) and resource utilization (fraction of time resources are active).^[10] These elements enable the simulation to replicate real-world variability while providing quantifiable insights into system behavior under different scenarios.^[13]

Historical Evolution

The origins of discrete event simulation (DES) trace back to the 1950s and 1960s, when early computing advancements enabled the development of specialized programming languages for modeling complex systems, particularly in military and operations research applications. In 1961, Geoffrey Gordon at IBM introduced the General Purpose System Simulator (GPSS), designed to facilitate rapid modeling of teleprocessing systems such as urban traffic control and telephone networks, marking a pivotal shift toward event-oriented simulation on mainframe computers like the IBM 709.^[14] This was followed in 1963 by SIMSCRIPT, developed by Harry Markowitz, Bernard Hausner, and Herbert Karr at the RAND Corporation, which emphasized event scheduling and entity-based modeling to address inventory and logistics problems, building on prior Monte Carlo methods but introducing structured languages for discrete events.^[15] These tools laid the groundwork for DES by formalizing the event-scheduling principles central to the paradigm, allowing simulations to advance time only at discrete occurrences rather than continuous intervals. The 1970s and 1980s saw significant expansion of DES amid growing computational accessibility and interdisciplinary adoption, with efforts toward standardization and enhanced usability. A key milestone was the 1976 introduction of the Discrete Event System Specification (DEVS) formalism by Bernard P. Zeigler, which provided a modular, hierarchical framework for modeling dynamic systems based on systems theory, enabling rigorous abstraction and composition of event-driven models independent of implementation details.^[16] The era also witnessed the integration of DES with emerging hardware, including the rise of graphical user interfaces in the 1980s that simplified model building and visualization, alongside standardization initiatives like the IFIP Working Group 7.3 on Computer System Modeling, established in 1973 to promote best practices across international research communities.^[17] These developments broadened DES applications from operations research to manufacturing and telecommunications, supported by languages influenced by SIMULA's object-oriented concepts discovered in the 1970s. In the 1990s and 2000s, DES evolved with advances in computing power, shifting toward object-oriented paradigms and multimethod approaches that combined discrete events with other modeling techniques for greater flexibility. This period incorporated 3D visualization capabilities post-1990s, driven by hardware improvements, allowing immersive representations of simulated processes in fields like logistics and engineering.^[18] Object-oriented extensions, inspired by earlier languages, facilitated reusable components and hierarchical modeling, enhancing scalability for large-scale systems. From the 2010s to 2025, DES has trended toward hybrid integrations with artificial intelligence and machine learning for predictive analytics, cloud-based platforms for distributed computing, and open-source ecosystems that democratize access. These advancements support real-time simulation in Industry 4.0 contexts, such as smart manufacturing and cyber-physical systems, where DES models dynamic interactions with IoT data streams to optimize resilience and efficiency.^[19]

Commercial Software

General-Purpose Tools

General-purpose discrete event simulation (DES) tools are commercial software packages designed for versatile application across diverse industries, such as manufacturing, logistics, and service operations, by supporting multimethod modeling that combines DES with other paradigms like agent-based and continuous simulation. These tools emphasize user-friendly interfaces, customization options, and integration capabilities to facilitate complex process modeling without domain-specific constraints. They enable users to simulate queueing systems, resource allocation, and workflow dynamics central to DES principles, allowing for what-if analyses and optimization in broad contexts.^[20] AnyLogic, developed by The AnyLogic Company, is a multimethod simulation platform that integrates DES, agent-based modeling, and system dynamics within a single environment. Its latest version, 8.9.6 released on September 8, 2025, introduces enhancements like a chart creation wizard for visualizing model statistics and improved 3D animation capabilities for immersive simulations. Key features include Java-based customization for advanced scripting, support for 3D visualization, and cloud-based experiment execution, making it suitable for large-scale, cross-industry applications such as supply chain optimization and urban planning.^[21][22] Arena, from Rockwell Automation, provides a flowchart-based interface for DES modeling focused on process flows, resource scheduling, and bottleneck identification. The current version, 16.20.10, includes modules for basic, advanced process, and healthcare templates, with integration to external tools like Microsoft Excel for data import/export. It features OptQuest for automated optimization of simulation parameters and supports animation for visual validation of models, enabling efficient analysis in manufacturing and service sectors.^[20][23] ExtendSim, produced by Imagine That Inc., offers hierarchical, block-oriented modeling for both DES and continuous systems, allowing modular construction of complex simulations. Version 2024.R1e, updated on July 8, 2025, enhances stability and adds features for discrete rate modeling and reliability block diagramming. Notable capabilities include built-in Monte Carlo simulation for uncertainty analysis, advanced statistical tools, and support for custom block development in C++, ideal for engineering and reliability assessments across industries.^[24][25] FlexSim, developed by FlexSim Software Products, Inc., specializes in 3D object-oriented DES with a drag-and-drop interface for intuitive model building. Version 25.2.0 (2025 Update 2), released August 16, 2025, introduces enhancements building on prior updates, including improved USD exports for interoperability and advanced AGV functionality. It excels in logistics and material handling simulations, featuring the Experimenter tool for scenario testing, process flow analysis, and real-time optimization, with strong emphasis on photorealistic 3D animations.^[26][27] Simio, by Simio LLC, employs an object-oriented approach to DES incorporating agent-based elements for dynamic entity behaviors and real-time decision logic. Version 19.280.49929, the latest public release as of 2025, supports seamless integration with ERP systems like SAP and Oracle for live data synchronization. Unique aspects include risk-based scheduling, 3D visualization with VR compatibility, and Python scripting for extended functionality, facilitating applications in production planning and supply chain management.^[28][29] SIMUL8, from SIMUL8 Corporation, uses an icon-based, drag-and-drop method for DES to model and improve operational processes. The 2025 Service Pack (version 32, build 5001) delivers updates like high-level analytics panels and enhanced process mining integration. It includes risk analysis tools for scenario evaluation, cloud collaboration for team-based modeling, and machine learning features for predictive insights, supporting process improvement in sectors from finance to healthcare.^[30][31]

Industry-Specific Tools

Industry-specific discrete event simulation (DES) software provides tailored solutions for sectors such as manufacturing, healthcare, and logistics, incorporating specialized libraries, 3D visualizations, and integrations to model domain-specific processes like patient flows, supply chains, and production lines. These tools emphasize sector adaptations, such as resource allocation in healthcare or material handling in ports, enabling users to optimize operations through scenario testing and risk analysis.^[32][33][34] Enterprise Dynamics, developed by InControl Simulation Solutions, focuses on material handling and logistics simulations, allowing users to create digital twins of supply chains, transportation systems, and manufacturing processes to enhance efficiency and reduce costs. It supports 2D and 3D modeling for complex infrastructures, including libraries tailored for airport and port operations, such as baggage handling and terminal flows. The software platform, updated through versions like 10.6.1 as of recent releases, integrates real-time data for predictive analytics in logistics networks.^[35][36] Plant Simulation, from Siemens PLM Software (now Siemens Digital Industries Software), specializes in factory layout optimization and production system analysis, using object-oriented 3D models to simulate material flows and resource utilization across manufacturing facilities. Version 2509, released October 2025, integrates seamlessly with Tecnomatix tools for production line balancing and advanced experiment management, including genetic algorithms and neural networks for scenario optimization. It supports interfaces like OPC UA and JT CAD data import for collaborative digital twin development.^[32][37] WITNESS, originally from Lanner Group Ltd. and now integrated into Royal HaskoningDHV's Twinn suite, targets manufacturing and supply chain modeling, enabling virtual representations of facilities to validate decisions on processes like AGV strategies and site logistics. Version 27, released in 2024, supports immersive 3D and VR visualizations for experimentation, allowing drag-and-drop creation of interactive models to communicate operational insights. It includes predictive simulation features for ROI analysis.^[38][39][40] anyLogistix, by The AnyLogic Company, is designed for supply chain network design, combining DES with optimization algorithms to simulate inventory management, transportation planning, and risk scenarios over time. Version 3.4.1, released November 10, 2025, features a map-based interface that facilitates end-to-end modeling, integrating GIS data and ERP systems for accurate forecasting of KPIs like costs and service levels. It introduces enhanced cloud deployment options for private servers, alongside iterations in network optimization experiments for faster comparisons.^[33][41] Simcad Pro, developed by CreateASoft, Inc., addresses lean manufacturing and healthcare processes, offering dynamic simulations for warehousing, automation, and patient care pathways with built-in value stream mapping. Key features include on-the-fly model adjustments during runs and a physics-based engine for realistic 2D/3D/VR animations, supporting interactive optimization without coding. Version 16.2 enhances multi-threaded processing and real-time data integration for healthcare-specific applications like emergency department flows.^[42] Care Pathway Simulator, from SAASoft Ltd., models healthcare services using DES to design and test patient care pathways, focusing on flow efficiency, queue management, and resource allocation in settings like outpatient clinics and emergency units. It assesses alternative configurations to minimize waits and optimize staffing, as demonstrated in NHS case studies for vascular surgery scheduling and endoscopy improvements. The tool applies value-stream modeling to evaluate whole-system impacts, such as hospital length-of-stay reductions.^[34] GoldSim, by GoldSim Technology Group LLC, supports risk analysis in environmental and project management domains, blending Monte Carlo methods with DES elements to simulate discrete events overlaid on continuous systems. GoldSim 15, released January 7, 2025, models uncertainties in complex scenarios, such as contaminant transport or mission reliability, using graphical influence diagrams for hierarchical, probabilistic forecasts. It includes expanded extension modules for financial and reliability analysis, aiding decision-making in over 600 organizations globally.^[43][44]

Open Source Software

Simulation Libraries

Simulation libraries in the context of open source discrete event simulation (DES) software provide programmatic frameworks that enable developers and researchers to integrate simulation capabilities directly into custom applications. These libraries emphasize flexibility for embedding DES models within larger software systems, often leveraging the host programming language's features for modeling entities, events, and processes. Unlike standalone applications, they focus on API-driven interactions, allowing for seamless extension and integration with domain-specific algorithms, such as optimization routines or data analysis pipelines.^[45] SimPy is a process-based DES library implemented in Python under the MIT license, with its latest stable release version 4.1.1 occurring on November 12, 2023. It supports modeling complex systems through coroutines that define entity behaviors, generators to handle event scheduling, and monitoring files for collecting runtime statistics like queue lengths or resource utilization. Core APIs include the Environment class, which manages simulation time via methods such as env.run(until=None) for executing until a specified time or event, and env.step() for advancing the simulation one step at a time, facilitating custom control in integrated applications. SimPy is particularly suited for use cases involving custom algorithm integration, such as simulating supply chain logistics with embedded machine learning for demand forecasting.^[46][47] DESMO-J is an object-oriented DES framework developed in Java and distributed under the Apache License 2.0, with the most recent version 2.5.1e released in March 2017. It provides hierarchical modeling through entities like simulators, models, and entities, incorporating an experiment framework that supports multiple replications, parameter variation, and output analysis for robust statistical validation. Visualization capabilities extend to 2D and 3D representations by interfacing with external tools like GraphStream or Java 3D, without built-in graphical interfaces. Key APIs revolve around the desmoj.core.simulator package, including TimeOperations for event timing and Experiment for running and analyzing simulations, enabling developers to embed DES in enterprise Java applications. Common use cases include integrating DES models with custom optimization algorithms in manufacturing process design.^[48] SystemC is an event-driven simulation library written in C++ under the SystemC Open Source License (a permissive OSI-approved license), with version 3.0.2 released on October 31, 2025.^[45] Designed primarily for hardware and software co-simulation, it features a kernel that schedules events via delta cycles and time advances, using modules, ports, channels, and events to model concurrent and hierarchical systems. This structure supports the Discrete Event System Specification (DEVS) formalism for modular, composable simulations. Representative APIs include sc_event::notify(delay) for scheduling events and sc_module for defining simulatable components, allowing fine-grained control in performance-critical environments. It excels in use cases like custom algorithm integration for system-on-chip verification, where DES models are embedded alongside hardware description languages.^[49] SNA is a lightweight DES library for C# and the .NET ecosystem, licensed under MIT, with version 1.0.6 released on September 14, 2025.^[50] It employs an entity-event model where entities interact through scheduled events, incorporating built-in tracing for event logging and support for parallel execution via .NET's Task Parallel Library to accelerate large-scale simulations. Core APIs center on the Simulation class, which handles event queues with methods like ScheduleEvent(time, action) and Run() for execution, alongside tracing utilities for performance profiling. This makes SNA ideal for integrating DES into custom .NET applications, such as real-time decision support systems with embedded heuristic algorithms for resource allocation.^[51] Ptolemy II is an actor-oriented modeling framework implemented in Java under the BSD license, with stable version 11.0 released on June 19, 2018. It facilitates heterogeneous simulations by combining DES with other paradigms like continuous-time or synchronous reactive models, using directors to enforce DES scheduling semantics—such as event queuing and priority handling—in multimodal workflows. Actors communicate via ports and relations, supporting compositional design for complex systems. Key APIs include the Director interface for simulation control (e.g., fire() for processing actors) and Workspace for model construction, enabling extensible integrations. Ptolemy II is commonly used for custom algorithm integration in embedded systems research, such as co-designing control algorithms with DES models for cyber-physical systems.

Standalone Applications

Standalone applications in open source discrete event simulation encompass complete, standalone executable programs equipped with graphical user interfaces, allowing users—particularly non-programmers—to build, run, and analyze models through intuitive drag-and-drop or visual editing tools rather than code integration. These tools emphasize ease of use for modeling complex systems like manufacturing lines, networks, or architectures, often incorporating visualization, optimization, and analysis features directly within the application. Unlike libraries that require embedding in custom code, standalone applications provide end-to-end workflows from model creation to simulation execution and result reporting. JaamSim is a cross-platform Java-based tool licensed under the Apache 2.0 license, with its most recent version released in September 2025. It supports drag-and-drop 3D modeling for constructing simulation scenarios, enabling scalable simulations of large systems such as factories or logistics networks. Key capabilities include built-in optimization for parameter tuning and Monte Carlo methods for uncertainty analysis, making it suitable for performance evaluation in industrial applications.^[8] CPN Tools operates primarily on Windows platforms under a dual license including GPLv2, with the last major update in version 4.0.1 released in February 2015. This tool specializes in Coloured Petri Nets as a modeling paradigm for discrete event simulation, facilitating the representation and simulation of concurrent processes in distributed systems. It includes state space analysis tools for verifying properties like boundedness and liveness, supporting both untimed and timed nets for detailed behavioral exploration.^[52] PowerDEVS is a C++-based application available for Linux and Windows, licensed under the Academic Free License (AFL) or GPLv2, with version 3.0 released on December 16, 2020.^[53] Built on the Discrete Event System Specification (DEVS) formalism, it enables hybrid modeling through atomic and coupled models, allowing hierarchical construction of simulations for continuous and discrete dynamics. The graphical interface supports visual editing of model components, ideal for engineering and research in complex system behaviors.^[54] gem5 is a C++ simulator runnable on Unix-like systems, released under the BSD 3-Clause license, featuring significant updates in version 24.0 during 2024. As a full-system discrete event simulator for computer architectures, it models CPU and GPU interactions with support for booting unmodified operating systems like Linux, including trace-based debugging for performance tracing. Recent enhancements extend to advanced interconnect protocols, such as AMBA-compatible interfaces, enhancing its utility in hardware design verification.^[55] SIM.JS runs in web browsers via JavaScript under the LGPL license, with its core implementation dating to around 2012 and no major updates since. This browser-based tool provides canvas-based visualization for discrete event simulations, utilizing an event queue to manage entity interactions and resources like facilities and buffers. It suits educational and lightweight web applications, offering built-in statistics collection and random number generation for probabilistic modeling.^[56]