Agent based modeling (ABM) is an abstraction scheme suitable to capture the complexity arising from entities that dynamically interact together and take decisions based on local knowledge and external events. These are valuable characteristics when dealing with agile production processes or dynamic supply chains.

The java Enterprise Simulator (jES by Pietro Terna) is the first attempt to to investigate how enterprises arise, behave and fall, how they interact and, finally, how we can improve them using the ABM abstraction. jES follows a classic ABM approach - grounded on  Swarm libraries - which is enriched with features targeting the enterprise.

It is under the vision and directions of Pietro Terna that we started to develop and improve AESOP, which is now merged with another simulation framework: Agent & Complexity in Python - ACP.

The project has been founded by the University of Bologna (dep. of Management Science). The University of Torino (dep. of Economics), Fondazione Bruno Kessler (FBK) in Trento and the Div. of Packaging & Logistics of Lund University participate as a co-funding partners.

AESOP characteristics and features

AESOP is a simulation framework aimed to production processes modeling. Compared to its ancestor - jES - it is lightweight, more general and written in Python. In addition, it is no longer bound to the Swarm libraries.

AESOP has been merged with ACP, which represents its new event driven core. In addition, ACP adds a network based representation of the agent relations and it is also responsible of its flexible and extensible agent configuration system. The AESOP’s scheduling mechanism - based on spreadsheet files - and the ‘recipe’ mechanism are integrated into the novel core and enriched with new features.

The basic goal of the simulator is to find bottlenecks in production process which could be hard to detect with traditional approaches (e.g., top-down). In addition, it can be used to speculate about “what-if” scenarios in order to suggest solution strategies.

Exploring organizational learning is another feature of the framework. The detection of patterns of activities leading to the emergence of routines is highlighted by the formation of a learning curve which can be plotted by the system. Learning curves would be a valuable tool for business planning, but they are hardly predictable. AESOP helps to understand the dynamic complexity of the curve formation.

A promising approach is to model and analyze the supply chain in a structured manner (network of relations). Essentially, it means showing how the structure evolves according to the actor’s behavior and external factors (e.g., market fluctuations, environmental issues, taxation growth...). This approach would help to understand the “sustainability” of a supply chain and to take action in order to improve it or mitigate a critical situation (e.g., network hubs having too much power) - this may imply social and/or political consequences.


In alphabetical order:

Guido Fioretti, Gian Paolo Jesi, Pietro Terna


  1. Dissecting and Understanding Supply Chains through Simulation: an Agent-based Approach. In Proc. 1st International Symposium on Applied Research in Technologies of Information and Communication (ARcTIC), Bologna, December 2012.

  2. From Men and Machines to the Organizational Learning Curve. In Mohamad Y. Jaber (ed.), Learning Curves: Theory, Models, and Applications, Chapter IV, pp. 57-70. Boca Raton, CRC Press 2011

  3. Merging Event-Based and Agent-Based Simulation: The ACP Framework (report)

  4. Organizational Learning with Agent Based Modeling (report)

Availability and Download

The software is currently in beta stage; it is available at SourceForge .