Discrete Event Simulation & Dynamic Process Simulation
Simulation models provide a virtual copy of a real-world system. Simulation is used to mimic the system’s behaviour and t better understand its processes.
The simulation model is the core of the simulation process.It shows the relation between several major process variables, such as service levels, resource or asset utilization, throughput rates and inventory stock levels. Simulation studies give an insight in the dynamism and variability of a system: two key elements that impact on all real processes and that are not so easily predictable.
If your process is complex, ensure to make decisions on solid bases. For more than 20 years we help organizations to make better decisions through simulation models.
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.
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 ACT OR we have a great experience in the development of dynamic simulators, also used as training tools for operators.
DYNAMIC PROCESS SIMULATION
With Modelica you can simulate the dynamic behavior and the complex interactions of a number of engineering systems including mechanic, electric, thermo-dynamic, hydraulic, pneumatic, thermal and control systems.
Discrete Event Simulation Software
Discrete Event Modeling Empowers the Optimization of Complex Processes
Continuous change is typical in the majority of processes, so modeling a large, complex process can be a daunting task. Discrete event modeling is the process of depicting the behavior of a complex system as a series of well-defined and ordered events and works well in virtually any process where there is variability, constrained or limited resources or complex system interactions.
Discrete event simulation allows you to quickly analyze a process or system’s behavior over time, ask yourself “why” or "what if" questions, and design or change processes or systems without any financial implications.
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Simio is a family of products that includes the Personal, Design, Team, and Enterprise Editions. Models built with all are fully compatible both up and down the product family. Design, Team and Enterprise Editions provide the same powerful 3D object-based modeling environment. Enterprise Editions also allows for the special view for schedules built in Enterprise Edition to be ran.
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Bloomy Decision Platform uses the power of artificial intelligence, predictive analytics, simulations
and mathematical optimization techniques
Find out more about Bloomy Decision Modules
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