DISCRETE EVENT SIMULATION
A discrete-event-simulation (DES) is used to represent and study the evolution of a system as a discrete sequence of events. In this type of models, the state of the system changes at specific instants over the time. The significant advantage provided by this type of simulation is the capability to capture the operation and the behavior of complex systems. Typically DES is used to analyze a supply chain, factories, distribution centers, warehouses, hospitals, etc. It is the best tool to support process re-engineering.
Being the real world often affected by randomness, like for example a failure, it is common, and good practice, to capture such randomness in the model. A stochastic model is a model in which a certain degree of unpredictability or randomness phenomena influences the outcome. All the stochastic models have the following in common:
- They reflect all aspects of the problem being studied
- Probabilities are assigned to events within the model
- Those probabilities can be used to make predictions or supply other relevant information about the process.
The capability to consider randomness in a study of a real system makes the analysis much more robust. A stochastic approach is used in several contexts, from the manufacturing to the insurances, the investments management, healthcare and so on. At ACT OR we use discrete event simulation (DES) and stochastic models to study and improve the performances in a variety of industries and systems. Simulators are also used in our Decision Science platform to support day-to-day what-if analysis.
ACT OR's SIMULATION PARTNERSHIP
Agent-based Modeling and Simulation (AMBS)
Agent-based modeling and simulation (ABMS) is a relatively new approach to modeling systems composed of autonomous, interacting agents. Agent-based modeling is a way to model the dynamics of complex systems and complex adaptive systems.
Agent-based models usually include the modeling of behaviors (for example of humans) and are used to observe the collective effects of agents interactions.
Such systems often self-organize themselves and create new knowledge. This approach is an excellent way to study and emergent behaviors, systems evolution or to predict trends and outcomes.
The development of agent modeling tools, the availability of micro-data, and the advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines.
Agent-Based Models can be used to design infrastructures and procedure to manage emergency situations, to study economic systems, markets, social and biological contexts, to explore, for example, the spread of a virus or the performance of a layout in an airport or a store.
At ACT OR we built powerful agent-based models for complex systems with a particular focus on the computational performance of such models.
Dynamic simulation models are used to describe the behavior of a system over the time. Such methods are described by ordinary differential equations or partial differential equations. The system can be a mechanical equipment or even a big plant.
The dynamic simulation models are often used in process control, to study, tune and improve automation and control logic.
At ACTOR we have a great experience in the development of dynamic simulators, also used as training tools for operators.
PROCESS DYNAMIC SIMULATION