What are the Differences Between Simulation Software: Discrete, Continuous, and Agent-Based?

Simulation has become an integral part of many industries due to its capacity to provide insight into complex operations and processes. This post deals with the different types of simulation software applications, their capabilities, and application. Here, discrete event, agent-based, and continuous simulation will be defined and the differences across all options highlighted to help enterprises make easy decisions when choosing a simulation software.

Definition

Discrete event simulation (DES) models the operation of a system as a sequence of discrete events that occur in different time intervals. The discrete events occur at specific points in time thus marking the ongoing changes of state within the modeled system.

Continuous simulation (CS) models the operations of a system to continuously track system responses through the duration of the simulation. This means results are produced at every point during the simulation and not in intervals. Continuous simulations also produce data in instances where no ongoing changes occur.

Agent-based models (ABM) simulate the actions and interactions of individual agents within a system. The agents can either be a piece of singular equipment or a group of assets working towards a similar goal. ABM simulations are run to determine the effects of these agents on the functions of the entire system an agent is a part of.

An example that highlights the functions of these different simulation techniques is that of a simple check out point in a supermarket. A DES model will view the arrival of a customer and the moment the customer departs as two separate events while the time spent will be represented as a time lapse between both events. The continuous simulation will continuously count the number of customers passing through the checkpoint and its general effect on the checkout system. The ABM simulation sees the customer and checkout point as autonomous agents and tracks their effect on the entire sales process.

With this explanation, it is easy to note that DES technique models physical phenomena or reality excellently as it is able to track occurring events. The agent-based and continuous options are excellent at determining the behavioral pattern of a system. In many cases, a combination of the different simulation techniques provides more-rounded results, especially when modeling complex processes with diverse variables and events.

The Differences in Agent-based, Discrete Event, and Continuous Simulation Features

To highlight these differences a few criteria will be used. These criteria include the following features:

  • What they simulate – This refers to the models they are best at simulating
  • Time step – This refers to how the techniques view the passage of time and time intervals
  • Queuing – This refers to how queue flows are managed
  • Statistical details – This refers to how they define or evaluate events within a system

What they Simulate

Starting with DES, as stated earlier, DES software applications are used to simulate discrete events, needs, and requirements. Continuous simulations are generally applied to flowing continuous processes while ABM is applied to autonomous agents and systems.

Time Step

For DES software, the time step changes according to the occurrence of individual events while for continuous simulation, time steps basically remain unchanged. In AGM software apps, time steps change according to the changing interactions of the autonomous agent.

Queuing

 DES software applies diverse techniques or systems to manage queues. This includes the use of a first-in-first-out (FIFO) approach or the last-in-first-out (LIFO) approach to managing queues. Continuous simulation software makes use of only the first in and first out system to manage queues. As for ABM, the management of queues is a bit different as it describes a system from the perspective of the agent. But a FIFO or LIFO system can be used to manage queues in ABM simulations.

The Differences in Application

Use cases provide realistic examples of defining or highlighting the differences encountered using these different simulation techniques. Starting with discrete event simulation, the discrete nature of this technique makes it an excellent choice for industrial simulations where events occur.

This includes the manufacturing industry, pharmaceutical production enterprises, plants, and industries with functional logistics systems. Here, the ability to simulate the arrival and departure of entities or queuing problems provide a level of insight into industrial operations in ways other methods cannot. An example is the use of Simio’s DES software to optimize activities within the Nebraska Medical Center. In this example, DES modeling was used to optimize hospital operations by reducing the travel time of surgeons and patients, as well as, the use of operating rooms across the medical facility.

Discrete event simulations are also powerful tools in capital intensive industries due to their ability to perform what-if analysis before pursuing further implementation initiatives. The experimentation capacity it brings to the table can save these enterprises from financial loses on specified business operations. The ability to also speed up or slow down specific phenomena to analyze expansive shifts or systems makes it a powerful tool for business applications.

Other application advantages DES software apps bring to the table include; its use as a training and validation tool in Industry 4.0, and its ability to kick start the digital transformation initiatives of enterprises.

Continuous Simulation Software – The continuous nature of this simulation technique makes it a unique tool for analyzing flowing processes or elements with non-linear relationships.

Continuous simulations are generally used within advanced engineering fields where simulator engines are designed. This includes the aviation industry for designing flight simulators, and autopilot programs. It is also used in designing gaming engines for video games such as the Nintendo Wii.

In industrial settings, discrete event simulation software applications are favored but continuous simulation is being used for generative design tasks and managing control systems in the pharmaceutical industry. It is also used in predicting or estimating the probability of natural phenomena such as the occurrence of flooding and hurricanes. These application examples mean continuous simulation is predominantly applied in STEM-related fields.

The advantages continuous simulation brings to the table include; the ability to describe systems with varying activities occurring within the same time interval. Continuous simulations are also used in enhancing artificial intelligence systems due to their theoretical analytical capabilities.

Agent-based Simulation Software – ABM models are generally used in the social sciences. It is extensively used to study interdependencies between different human activities, social and economic systems, and in facilities where the interactions between diverse systems define operations.

The three concepts that define the application of ABM are its flexibility, its ability to capture emergent phenomena, and its ability to define systems. With these abilities comes certain advantages such as the ability to integrate ABM simulations into DES or continuous simulation environments.

Its ability to simulate interactions between autonomous agents also makes it an excellent tool for understanding shop floor behavior. For example, can be used to analyze the cause of shop floor traffic across a facility where both humans and autonomous machines interact. Here, it’s individualistic approach to simulation provides different perspectives from active agents explaining the cause of phenomena such as an unexpected traffic jam within a system.

ABM is actively used in to monitor flowing process such as traffic and customer flow management within physical shops, parks, and recreational centers. An example is its use in a Macy’s store. In this example, ABM was used to estimate the distribution of sales people within its facility and how they interact with customers to enhance its operations.

It is also used to analyze stock market phenomena and operational risk within organizations in diverse industrial niches. Thus, highlighting the versatility and flexibility ABM simulations bring to diverse interactive processes.

In Summary

Simulation provides insight into human relationships, industrial processes, urban and regional planning, and complex systems across every niche. Thus cementing its status as a major data analytics and digital transformation tool designed for every organization.

Although DES, CS, and ABM simulations apply different approaches to simulation, the results they produce optimize human and industrial endeavors in different ways. These ways include planning and implementation, enhancing customer relationships, training staff, developing strategies, and design. The Simio modeling and Simulation software provides an intuitive platform for modeling, running, managing, and sharing DES, CS, and ABM simulations to optimize your organization’s operational processes. You can learn more about specific use cases by browsing through our catalog of case studies.

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