[26] The Distributed Computer Network (DCN) research group at Saint Petersburg Polytechnic University developed a software system for the analysis of program correctness; the new tool was named COVERS (Concurrent Verification and Simulation).

In 1998, the success of this research inspired the DCN laboratory to organize a company with the mission of developing a new generation of simulation software.

Development emphasis was placed on applied methods: simulation, performance analysis, behavior of stochastic systems, optimization, and visualization.

The resulting software was released in 2000 and featured the latest information technologies: an object-oriented approach, elements of the UML standard, the use of Java, and a modern GUI.

[42] Subsequent versions continued to enhance these features, allowing for more complex and multi-level simulations, such as buildings and automated guided vehicle systems.

[43] AnyLogic 8.7 expanded capabilities with overhead cranes, pedestrian area simulations with capacity restrictions, and support for social distancing rules.

[45] In AnyLogic 8.9.2, the NVIDIA Omniverse Connector was introduced, allowing users to link their simulations with detailed animations.

This connection lets users export 3D models from AnyLogic to NVIDIA Omniverse, enhancing the visual quality and facilitating collaboration.

[55] AnyLogic allows users to import CAD drawings as DXF files, and then visualize models on top of them.

AnyLogic software also supports 3D animation and includes a collection of ready-to-use 3D objects for animation related to different industries, including buildings, road, rail, maritime, transport, energy, warehouse, hospital, equipment, airport-related items, supermarket-related items, cranes, and other objects.

As an option, an exported AnyLogic model can be built into other pieces of software and work as an additional module to ERP,[63] MRP, and TMS systems.

It allows users to train AI agents, incorporate machine learning models into simulations, and generate synthetic data for various purposes.

By testing AI solutions in a simulated setting, AnyLogic reduces risks and ensures smoother implementation in real-world systems.

[72] Alpyne makes it possible to interact with AnyLogic models directly from Python, providing more control over reinforcement learning experiments.

It allows users to store, access, run, and share simulation models online, as well as analyze experiment results.

[77][1] Further enhancements with AnyLogic 8.1 included new plotting capabilities, improved 3D animations, and tools for collaborative editing and community engagement within the Cloud.

[83] The year after, Cloud transitioned to Java 11 and Angular 13 for the web UI, alongside several security updates for third-party libraries to improve performance and fix vulnerabilities.

Private Cloud instances gained the ability to manage user authentication via LDAP and Active Directory servers.

Early versions introduced features such as IBM ILOG CPLEX® for optimized network configurations, dedicated fleets, and flexible transportation routing.

Over time, significant functionalities were added, such as the Safety Stock Estimation and Capacitated Transport Optimization experiments, refining inventory management and delivery routes.

[90] The introduction of anyLogistix Studio Edition streamlined extension development and facilitated the creation of digital twins, blending detailed modeling with real-time data.

Versions like 2.13 and 2.15 emphasized network optimization, introducing database scenario import/export features to support collaboration and complex data integration.

[91][92] The development team’s focus shifted towards anyLogistix 3.0, launched in 2022, which transformed the software with a new tech stack, improved client-server architecture, and enhanced user experience.

[93] This version allowed running experiments from browsers and provided cross-platform access, catering to Windows, macOS, and Linux users.

Subsequent updates, like 3.1, introduced multi-user access and an API for integration, enhancing collaborative capabilities and performance for complex supply chain modeling projects.