From Wikipedia, the free encyclopedia
Jump to navigation Jump to search
AnyLogic 7 vector logo.svg
Developer(s)The AnyLogic Company (former XJ Technologies)
Initial release2000 [1]
Stable release
8.6 Professional [2] / August 2020 [3]
Written inJava SE
Operating systemWindows, macOS, Linux
Available inEnglish, Portuguese, Russian, German, Chinese, Spanish
TypeSimulation software

AnyLogic is a multimethod simulation modeling tool developed by The AnyLogic Company (former XJ Technologies).[4] It supports agent-based, discrete event, and system dynamics simulation methodologies.[5] AnyLogic is a cross-platform simulation software because it works on Windows, macOS and Linux.[5]

AnyLogic is used to simulate: markets and competition,[6] healthcare,[7][8] manufacturing,[9] supply chains and logistics,[10][11] retail,[12][13] business processes,[14] social [15] and ecosystem dynamics,[16] defense,[17] project and asset management,[18] pedestrian dynamics [19] and road traffic,[20] IT,[21] aerospace.[22]

History of AnyLogic[edit]

In the beginning of the 1990s there was a big interest in the mathematical approach to modeling and simulation of parallel processes. This approach may be applied to the analysis of correctness of parallel and distributed programs.[23] The Distributed Computer Network (DCN) research group at Saint Petersburg Polytechnic University developed such a software system for the analysis of program correctness; the new tool was named COVERS (Concurrent Verification and Simulation). This system allowed graphical modeling notation for system structure and behavior. The tool was applied for the research granted by Hewlett-Packard.

Three business simulation approaches

In 1998 the success of this research inspired the DCN laboratory to organize a company with a mission to develop a new age simulation software. The emphasis in the development was placed on applied methods: simulation, performance analysis, behavior of stochastic systems, optimization and visualization. New software released in 2000 was based on the latest advantages of information technologies: an object-oriented approach, elements of the UML standard, the use of Java, a modern GUI, etc. [24]

The tool was named AnyLogic, because it supported all three well-known modeling approaches: system dynamics,[15] discrete event simulation,[25] Agent-based modeling.[26] and any combination of these approaches within a single model. [27][28] The first version of AnyLogic was AnyLogic 4,[29] because the numbering continues the numbering of COVERS 3.0.

AnyLogic 5 was released in 2003. New version was focused on business simulation in different industries.[30]

AnyLogic 7, was released in 2014.[31] Being the biggest release for 7 years, it featured many updates aimed at simplifying model building, including support for multimethod modeling, decreased need for coding, renewed libraries, and other usability improvements. AnyLogic 7.1, also released in 2014, included the new GIS implementation in the software: in addition to shapefile-based maps, AnyLogic started to support tile maps from free online providers, including OpenStreetMap. [32]

2015 marked the release of AnyLogiс 7.2 with the built-in database and the Fluid Library.[33] Since 2015, AnyLogic Personal Learning Edition (PLE) is available for free for the purposes of education and self-education. The PLE license is perpetual, but created models are limited in size.[34]

The new Road Traffic Library was introduced in 2016 with AnyLogic 7.3.[35]

AnyLogic 8 was released in 2017. Beginning with Version 8.0, the AnyLogic model development environment was integrated with AnyLogic Cloud, a web service for simulation analytics.[36][1]

The platform for AnyLogic 8 model development environment is Eclipse. [37]

AnyLogic and Java[edit]

How simulation approaches correspond to the level of abstraction

AnyLogic includes a graphical modeling language and also allows the user to extend simulation models with Java code. [37] The Java nature of AnyLogic lends itself to custom model extensions via Java coding [38] The Professional version allows for the creation of Java runtime applications which can be distributed to users.

Multimethod simulation modeling[edit]

AnyLogic models can be based on any of the main simulation modeling paradigms: discrete event or process-centric (DE),[39] systems dynamics (SD),[40] and agent-based (AB).[6]

System dynamics and discrete event are traditional simulation approaches, agent based is a newer one. Technically, system dynamics approach deals mostly with continuous processes whereas discrete event and agent-based models work mostly in discrete time, i.e. jump from one event to another.

System dynamics dealing with aggregates is obviously used at the highest abstraction level. Discrete event modeling is used at low to middle abstraction. As for agent based modeling, this technology is used across all abstraction levels, and agent may model objects of very diverse nature and scale: at the "physical" level agents may be e.g. pedestrians or cars or robots, at the middle level – customers, at the highest level – competing companies.

AnyLogic allows the modeler to combine these simulation approaches within the same model. [7] As an example, one could create a model of the package shipping industry where carriers are modeled as agents acting/reacting independently whereas the inner workings of their transport and infrastructure networks could be modeled with discrete event simulation. Similarly, one can model consumers as agents whose aggregate behavior feed a systems dynamics model capturing flows such as revenues or costs which do not need to be tied to individual agents. This mixed language approach is directly applicable to a wide variety of complex modeling problems that may be modeled via any one approach albeit with compromises.


Simulation language[edit]

Simulation language constructions provided by AnyLogic

The AnyLogic simulation language consists of following items:

  • Stock & Flow Diagrams are used for System Dynamics modeling. [41]
  • Statecharts are used mostly in Agent Based modeling to define agent behavior. They are also often used in Discrete Event modeling, e.g. to simulate machine failure. [42][43]
  • Action charts are used to define algorithms. They may be used in Discrete Event modeling, e.g. for call routing, or in Agent Based modeling, e.g. for agent decision logic. [44][45]
  • Process flowcharts are the basic construction used to define process in Discrete Event modeling. Looking at this flowchart you may see why Discrete Event style is often called Process Centric. [46]

The language also includes: low level modeling constructions (variables, equations, parameters, events etc.), presentation shapes (lines, polylines, ovals etc.), analysis facilities (datasets, histograms, plots), connectivity tools, standard images, and experiments frameworks.

AnyLogic libraries[edit]

AnyLogic includes the following standard libraries:

  • The Process Modeling Library is designed to support DE simulation in Manufacturing, Supply Chain, Logistics and Healthcare areas. Using the Process Modeling Library objects you can model real-world systems in terms of entities (transactions, customers, products, parts, vehicles, etc.), processes (sequences of operations typically involving queues, delays, resource utilization), and resources. The processes are specified in the form of flowcharts. The Process Modeling Library is a successor of the Enterprise Library from AnyLogic 6, which is also available in AnyLogic 7. [47]
  • The Pedestrian Library is dedicated to simulating pedestrian flows in a physical environment. It allows you to create models of pedestrian-intensive buildings (like subway stations, security checks etc.) or streets (large numbers of pedestrians). Models support statistics collection on pedestrian density in different areas. This ensures acceptable performance of service points with a hypothetical load, estimates lengths of stay in specific areas, and detects potential problems with interior geometry – such as the effect of adding too many obstacles – and other applications. In models created with the Pedestrian Library, pedestrians move in continuous space, reacting to different kinds of obstacles (walls, different kinds of areas), as well as other pedestrians. Pedestrians are simulated as interacting agents with complex behavior, but the AnyLogic Pedestrian Library provides a higher level interface for faster creation of pedestrian models in the style of flowcharts. [19][48]
  • The Rail Library supports modeling, simulating, and visualizing operations of a rail yard of any complexity and scale. The rail yard models can be combined with discrete event or agent based models related to: loading and unloading, resource allocation, maintenance, business processes, and other transportation activities.[49]
  • The Fluid Library allows the user to model storage and transfer of fluids, bulk goods, or large amounts of discrete items, which are not desirable to model as separate objects. The library includes blocks such as tank, pipeline, valve, and objects for routing, merging, and diverging the flow. To improve model execution speed, the Fluid Library uses a linear programming solver. The library is designed to improve AnyLogic use in manufacturing, oil, gas, and mining industries. The user can simulate oil pipes and tanks, ore, coal conveyors, and production processes where liquids or bulk materials are involved, for example, in concrete manufacturing. [50]
  • The Road Traffic Library allows users to simulate vehicle traffic on roads. The library supports detailed, physical level modeling of vehicle movement. Each vehicle represents an agent that can have its own behavioral patterns inside. The library allows users to simulate vehicle movement on roads, taking into account driving regulations, traffic lights, pedestrian crossings, priorities at junctions, parking lots, and public transport movements. The library is suitable for modeling highway traffic, street traffic, on-site transportation at manufacturing sites, or any other systems with vehicles, roads, and lanes. A special traffic density tool is included to help analyze road network loads. [51]
  • The Material Handling Library assists in process simulation in factories and warehouses. The library contains conveyors, transporters, and other elements simplifying the creation of detailed production models.

Besides these standard libraries users can create their own ones and distribute them. [52][53]

Model animation[edit]

AnyLogic supports interactive 2D and 3D animation. [48] AnyLogic allows users to import CAD drawings as DXF files, and then visualize models on top of them. [54] This feature can be used for animating processes inside objects like factories, warehouses, hospitals, etc. This functionality is mostly used in Discrete Event (process-based) models in manufacturing, healthcare, civil engineering, and construction. 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.

Models can include custom UI for users to configure experiments and change input data.

Geospatial models, GIS integration[edit]

AnyLogiс models can use maps as a layout, which is often required by supply chains, logistics, and transportation industries. AnyLogic software supports the traditional shapefile-based map standard, SHP by Esri. In addition, AnyLogic supports tile maps from free online providers, including OpenStreetMap. Tile maps allow the modeler to use map data in models and to automatically create geospatial routes for agents. The main tile map features in AnyLogic include:

  • The model can access all of the data stored along with online-based maps: cities, regions, road networks, and objects (hospitals, schools, bus stops, etc.). [55]
  • Agents can be placed in specified points on the map, and moved along existing roads or routes.
  • Users can create the required elements inside the model using the built-in search.

Model integration with other IT-infrastructure[edit]

An AnyLogic model can be exported as a Java application, that can be run separately, or integrated with other software. As an option, an exported AnyLogic model can be built into other pieces of software and work as an additional module to ERP,[56] MRP, and TMS systems. Another typical use is integration of an AnyLogic model with TXT, MS Excel,[57] or MS Access files and databases (MS SQL, MySQL, Oracle, etc.). Also, Anylogic models include their own databases based on HSQLDB.

AnyLogic Cloud[edit]

AnyLogic Cloud is a web service for simulation analytics. It allows users to store, access, run, and share simulation models online, as well as analyze experiment results.

Using AnyLogic model development environment, developers can upload their models to AnyLogic Cloud and set up sharable web dashboards to work with models online. These dashboards can contain configurable input parameters and output data in the form of charts and graphs. Model users can set input data on the dashboard screen, run the model, and analyze the output.

AnyLogic Cloud allows users to run models using web browsers, on desktop computers and mobile devices, with the model being executed on the server side. Multiple run experiments are performed using several nodes. The results of all executed experiments are stored in the database and can be immediately accessed. Models can be run both with and without HTML5-based interactive animation. [58]

Developers can choose whether they want their models to be private or publicly available in the model library, which includes models from other AnyLogic users.

anyLogistix supply chain optimization software[edit]

The AnyLogic Company converted its development efforts for the supply chain domain in a separate software tool – anyLogistix. This spin-off product was introduced in 2014 as AnyLogic Logistics Network Manager and was renamed anyLogistix in 2015.

anyLogistix is based on the AnyLogic and CPLEX engines, GIS, and the new industry-oriented GUI. It also includes algorithms and techniques specific for supply chain design and optimization. Like other simulation software such as Arena and SIMUL8, optimization functionality is provided by OptQuest.[59] anyLogistix is fully integrated with AnyLogic, for instance, AnyLogic can be used for customization of objects inside anyLogistix, including warehouses, production sites, suppliers, inventory, sourcing, and transportation policies.

See also[edit]


  1. ^ a b AnyLogic Timeline the official web-site.
  2. ^ The release news on the official web-site.
  3. ^ The release news on the official web-site.
  4. ^ Evgrafov, Alexander N. (2017-03-23). Advances in Mechanical Engineering: Selected Contributions from the Conference "Modern Engineering: Science and Education", Saint Petersburg, Russia, June 2016. Springer. ISBN 978-3-319-53363-6.
  5. ^ a b Christopher W. Weimer, J. O. Miller, Raymond R. Hill. "Agent-Based Modeling: an Introduction and Primer" Proceedings of the 2016 Winter Simulation Conference
  6. ^ a b Jingsi Huang, Lingyan Liu, Leyuan Shi. "Auction Policy Analysis: an Agent-Based Simulation Optimization Model of Grain Market" Proceedings of the 2016 Winter Simulation Conference
  7. ^ a b Anatoli Djanatliev, Reinhard German, Peter Kolominsky-Rabas. "Hybrid Simulation with Loosely Coupled System Dynamics and Agent-based Models for Prospective Health Technology Assessments" Proceedings of the 2012 Winter Simulation Conference
  8. ^ Joe Viana, Stuart Rossiter, Andrew A. Channon, Sally C. Brailsford, Andrew Lotery. "A Multi-Paradigm, Whole System View of Health and Social Care for Age-Related Macular Degeneration" Proceedings of the 2012 Winter Simulation Conference
  9. ^ Thomas Felberbauer, Klaus Altendorfer, Alexander Hübl. "Using a Scalable Simulation Model to Evaluate the Performance of Production System Segmentation in a Combined MRP and Kanban System" Proceedings of the 2012 Winter Simulation Conference
  10. ^ Ilmarts Dukulis, Gints Birzietis, Daina Kanaska. "Optimization Models for Biofuel Logistic System" Engineering for Rural Developments, Jelvaga, 29–30 May 2008
  11. ^ Christian Wartha, Momtchil Peev, Andrei Borshchev, Alexei Filippov. "Decision Support Tool – Supply Chain" Proceedings of the 2002 Winter Simulation Conference
  12. ^ Peer-Olaf Siebers, Uwe Aickelin, Helen Celia, Chris W. Clegg. "Understanding Retail Productivity by Simulating Management Practices" Archived 2011-07-18 at the Wayback Machine EUROSIM-2007, September 2007
  13. ^ Peer-Olaf Siebers, Uwe Aickelin, Helen Celia, Chris W. Clegg. "A Multi-Agent Simulation of Retail Management Practices" Archived 2009-12-28 at the Wayback Machine Proceedings of the Summer Computer Simulation Conference 2007 (SCSC 2007)
  14. ^ Arnold Greenland, David Connors, John L. Guyton, Erica Layne Morrison, Michael Sebastiani. "IRS Post-Filing Processes Simulation Modeling: A Comparison of DES with Econometric Microsimulation in Tax Administration" Proceedings of the 2007 Winter Simulation Conference
  15. ^ a b Sergio E. Quijada, Juan F. Arcas, Cristian Renner, Luis Rabelo. "A Spatio Temporal Simulation Model for Evaluating Delinquency and Crime Policies" Proceedings of the 2005 Winter Simulation Conference
  16. ^ Datu Buyung Agusdinata. "Agent-Based Simulation of The Diffusion Dynamics and Concentration of Toxic Materials from Quantum Dots-Based Nanoparticles" Proceedings of the 2015 Winter Simulation Conference
  17. ^ Kyuhyeon Shin, Hochang Nam, Taesik Lee. "Communication Modeling for a Combat Simulation in a Network Centric Warfare Environment" Proceedings of the 2013 Winter Simulation Conference
  18. ^ Benny Tjahjono, Evandro Leonardo Silva Teixeira, Sadek Crisóstomo Absi Alfaro. "An Online Simulation to Link Asset Condition Monitoring and Operations Decisions in Through-Life Engineering Services" Proceedings of the 2013 Winter Simulation Conference
  19. ^ a b Khaled Nassar, Ahmed Bayyoumi. "A Simulation Study of The Effect of Mosque Design on Egress Times" Proceedings of the 2012 Winter Simulation Conference
  20. ^ Xiaobing Li, Asad J. Khattak, Airton G. Kohls. "Signal Phase Timing Impact on Traffic Delay and Queue Length-A Intersection Case Study" Proceedings of the 2016 Winter Simulation Conference
  21. ^ Bojan Spasic, Bhakti S. S. Onggo. "Agent-Based Simulation of The Software Development Process: A Case Study at AVL" Proceedings of the 2012 Winter Simulation Conference
  22. ^ Benjamin Schumann, James Scanlan, Hans Fangohr. "Complex Agent Interactions in Operational Simulations for Aerospace Design" Proceedings of the 2012 Winter Simulation Conference
  23. ^ Rodd, M. G.; Knuth, E. (2014-07-04). Distributed Databases in Real-Time Control. Elsevier. ISBN 978-1-4832-9836-8.
  24. ^ Albert Molderink, Maurice G.C. Bosman, Vincent Bakker, Johann L. Hurink, Gerard J.M. Smit. "Simulating the Effect on the Energy Efficiency of Smart Grid Technologies" Proceedings of the 2009 Winter Simulation Conference
  25. ^ Sudhanshu S Singh, Rakesh R Pimplikar, Ritwik Chaudhuri, Gyana Parija. "Outplacement Time and Probability Estimation Using Discrete Event Simulation" Proceedings of the 2016 Winter Simulation Conference
  26. ^ Cynthia Nikolai, Gregory Madey. "Tools of the Trade: A Survey of Various Agent Based Modeling Platforms", Journal of Artificial Societies and Social Simulation vol. 12, no. 2 2, 31 March 2009
  27. ^ Andrei Borshchev, Alexei Filippov. "From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools", The 22nd International Conference of the System Dynamics Society, July 25–29, 2004, Oxford, England
  28. ^ Peter Bazan, Reinhard German. "Hybrid Simulation of Renewable Energy Generation and Storage Grids" Proceedings of the 2012 Winter Simulation Conference
  29. ^ Andrei Borshchev. "AnyLogic 4.0: Simulating Hybrid Systems with Extended UML-RT", Simulation News Europe - EUROSIM 2001
  30. ^ Zlatanovska, Biljana; Stojkovic, Natasha; Kocaleva, Mirjana; Stojanova, Aleksandra; Lazarova, Limonka; Gobubovski, R. (2018-05-01). "Modeling of some chaotic systems with anylogic software". TEM Journal. 7: 465–470. doi:10.18421/TEM72-31.
  31. ^ The news on the company’s official website.
  32. ^ The news on the company's website
  33. ^ The news on the company’s official website
  34. ^ The news on the company's website
  35. ^ The news on the company’s official website
  36. ^ Release notes on the developer's official website.
  37. ^ a b Bin Li, Wen-feng Li. "Modeling and Simulation of Container Terminal Logistics Systems Using Harvard Architecture and Agent-Based Computing". Proceedings of the 2010 Winter Simulation Conference
  38. ^ Carol C. Menassa, Feniosky Peña Mora. "Real Options and System Dynamics Approach to Model Value of Implementing A Project Specific Dispute Resolution Process in Construction Projects". Proceedings of the 2009 Winter Simulation Conference
  39. ^ Luís M. S. Dias, António A. C. Vieira, Guilherme A. B. Pereira, José A. Oliveira. "Discrete Simulation Software Ranking – A Top List of The Worldwide Most Popular and Used Tools". Proceedings of the 2016 Winter Simulation Conference
  40. ^ Marco Pruckner, David Eckhoff, Reinhard German. "Modeling Country-Scale Electricity Demand Profiles". Proceedings of the 2014 Winter Simulation Conference
  41. ^ Mario Marin, Luz Alba Andrade, Yanshen Zhu, Erwin Atencio, Carlos Boya. "Supply Chain and Hybrid Modeling: The Panama Canal Operations and Its Salinity Diffusion". Proceedings of the 2010 Winter Simulation Conference
  42. ^ Hui Xi, Seungho Lee, Young-Jun Son. "An Integrated Pedestrian Behavior Model Based on Extended Decision Field Theory and Social Force Model". Proceedings of the 2010 Winter Simulation Conference
  43. ^ Anatoli Djanatliev, Reinhard German. "Prospective Healthcare Decision-Making by Combined System Dynamics, Discrete-Event and Agent-Based Simulation". Proceedings of the 2013 Winter Simulation Conference
  44. ^ Mostafa Batouli, Ali Mostafavi. "A Hybrid Simulation Framework for Integrated Management of Infrastructure Networks". Proceedings of the 2014 Winter Simulation Conference
  45. ^ Jin Zhu, Ali Mostafavi. "Integrated Simulation Approach for Assessment of Performance in Construction Projects: A System-Of-Systems Framework". Proceedings of the 2014 Winter Simulation Conference
  46. ^ Magdy Helal. "A Hybrid System Dynamics-Discrete Event Simulation Approach to Simulating the Manufacturing Enterprise". Electronic Theses and Dissertations, University of Central Florida, 2008
  47. ^ Anatoli Djanatliev, Peter Bazan, Reinhard German. "Partial Paradigm Hiding and Reusability in Hybrid Simulation Modeling Using the Frameworks Health-Ds and I7-Anyenergy". Proceedings of the 2014 Winter Simulation Conference
  48. ^ a b Martin Jung, Axel B. Classen, Florian Rudolph. "Creating and Validating A Microscopic Pedestrian Simulation to Analyze an Airport Security Checkpoint". Proceedings of the 2015 Winter Simulation Conference
  49. ^ The news on the developer's official website
  50. ^ The news on the developer's official website
  51. ^ The news on the developer's official website
  52. ^ Jingjing Yuan, Thomas Ponsignon. "Towards a Semiconductor Supply Chain Simulation Library (SCSC-SIMLIB)". Proceedings of the 2014 Winter Simulation Conference
  53. ^ Anatoli Djanatliev, Reinhard German. "Towards a Guide to Domain-Specific Hybrid Simulation". Proceedings of the 2015 Winter Simulation Conference
  54. ^ Fabian Zambrano, Pablo Concha, Francisco Ramis, Liliana Neriz. "Improving Patient Access to A Public Hospital Complex Using Agent Simulation". Proceedings of the 2016 Winter Simulation Conference
  55. ^ A.G. Demin. "Creation of suburban passenger transportation simulation model". Proceedings of the 2017 IMMOD Conference
  56. ^ Sanjay Jain, David Lechevalier. "Standards Based Generation of a Virtual Factory Model". Proceedings of the 2016 Winter Simulation Conference
  57. ^ Korovin M.A., Zahodyakin G.V. "Simulation modeling improving the efficiency of the slab storage of a smelting furnace". Proceedings of the 2017 IMMOD Conference
  58. ^ Borshchev A.V. "Migration of Simulation Modeling to the Cloud". Proceedings of the 2017 IMMOD Conference
  59. ^ "Creating an Optimization Experiment". AnyLogic Help. AnyLogic. Retrieved 23 August 2020.

Further reading[edit]

External links[edit]