Visualizing an organizations system and its functions through the lens of its operational machines, equipment or assets brings opens up a unique perspective to stakeholders. This is what agent-based simulation models intend to accomplish.
In agent-based modeling, active agents within a system are identified and their behavioral patterns mapped out. The connections between them are also established and these parameters provide the framework in which the simulation system is designed on. Variables are then introduced into these systems and simulations can then be run to view the dynamic relationships across every agent that defines the shop floor. This technique is also referred to as individual-based simulation to take into account the different entities that are important to a shop floor's operation.
In many industries, the overall equipment efficiency (OEE) determines the level of productivity within a facility. Thus, the ability to make operational equipment the fulcrum of your simulation springs up interesting insights into how manufacturing enterprises and agent-based facilities function. One example is in Industry 4.0 where the use of autonomous equipment such as automated guided vehicles, edge computing devices, and IIoT is rampant.
Here, agent-based simulations put the large data sets produced by these autonomous agents to work by understanding how they communicate and their special relationship within a facility.
Application of Agent-based Simulation
This simulation technique is widely used in environments with multiple individual agents. This means enterprises in the manufacturing, health-care, logistics, and mining industries consistently make use of agent-based simulations for predictive analysis and to receive business insight. It is also applied in economics and the social sciences to better understand individual and complex relationships within environmental systems.
These simulations are used in discovering patterns across agent behaviors and these patterns can be used for advanced planning and experimentation within a digital environment before implementation. It can also be used to simulate how a system of multiple agents reacts to different scenarios. Thus, providing insight into the effect of individual machine downtime or multiple machine downtime to the entire system.
Agent-Based Modeling and Simulation with Simio
Simio offers enterprises with the opportunity to define behavioral patterns across their business systems through agent-based simulations. With the Simio software, you can also integrate data from enterprise relationship systems, customer relationship systems, and other databases to create dynamic simulation models that deliver accurate results.
Simio also supports the combination of agent-based simulations with discrete event simulations to optimize the system models of a facility. For example, facilities that use automated material handling equipment within supply chains can view the supply chain as an agent. Simio can create discrete event models of the entire facilities operations while also developing an agent-based simulation model for the supply chain. The combination of both models will optimize simulation results and provide accurate insights into the diverse relationships within such a facility.
The combination of both simulation models and techniques is powerful and helps with capturing the non-linear dynamics that occur in diverse industries. You can learn more about agent-based modelling and multi-method modeling by requesting a demonstration.