Modeling is a powerful tool. With it, you can analyze, design, and operate complex systems. You use models to assess real-world processes too complex to analyze via spreadsheets or flowcharts, testing hypotheses at a fraction of the cost of undertaking the actual activities. An efficient communication tool, modeling shows how an operation works and stimulates creative thinking about how to improve it. Models in industry, government, and educational institutions shorten design cycles, reduce costs, and enhance knowledge.
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Why simulation is important

Simulation involves designing a model of a system and carrying out experiments on it as it progresses through time. Models enable you to see how a real-world activity will perform under different conditions and test various hypotheses at a fraction of the cost of performing the actual activity.

One of the principal benefits of a model is that you can begin with a simple approximation of a process and gradually refine the model as your understanding of the process improves. This “step- wise refinement” enables you to achieve good approximations of very complex problems surprisingly quickly. As you add refinements, the model more closely imitates the real-life process.

 

As stated by Rene Alvarez of Smart Simulations, a "computer simulation consists of digitally mimicking real systems using computers and specialized software".

Systems, models, and simulation

All professions use models of one form or another. But the word "model" does not always have the same meaning to business professionals, managers, scientists, and engineers.

Systems

The real world can be viewed as being composed of systems. A system is a set of related components or entities that interact with each other based on the rules or operating policies of the system:

arrow Entities are the internal components of the system. Entities are involved in processes—activities in which they interact with each other.
arrow Operating policies—the types of controls and availability of resources—are the external inputs to the system. They govern how the system operates and thus how the entities interact.

Over time, the activities and interactions of entities cause changes to the state of the system; this is called system behavior or dynamics. Systems can be mathematically straightforward, such as a flower growing in the soil and turning towards the sun to maximize photosynthesis. Definition of a modelOr they can be more complex, such as supply chain operations composed of planning, selling, distribution, production, and sourcing subsystems.

Models

A model is an abstracted and simplified representation of a system at one point in time. Models are an abstraction because they attempt to capture the realism of the system. They are a simplification because, for efficiency, reliability, and ease of analysis, a model should capture only the most important aspects of the real system. Most models can be classified into four basic types:

arrow A scaled representation of a physical object, such as a 1:18 diecast model of a Ferrari, a clay model of a proposed packaging bottle, or a scale model of the solar system.
arrow A graphical or symbolic visualization, such as a flow chart of office procedures, the board game Monopoly (which represents the hotels and facilities of Atlantic City), or an architect’s plans for a building.
arrow An analytical or mathematical formula that yields a static, quantitative solution. For instance, an analytic model might consist of several independent sample observations that have been transformed according to the rules of the model. Common examples of analytic models are spreadsheet models or linear programming models.
arrow A mathematical description that incorporates data and assumptions to logically describe the behavior of a system. This type of model is typically dynamic—it has a time component and shows how the system evolves over time. ExtendSim products are tools for building mathematically-based, dynamic models of systems.

Dynamic modeling is the foundation for computer modeling. Thus, the word “model” will be used to mean a description of the dynamic behavior of a system or process.

ExtendSim models typically have a time component and can show cause and effect and the flow of entities throughout a system (you can also create ExtendSim animations that show spatial relationships.)

Simulation

The Merriam-Webster OnLine Dictionary defines simulation as “the imitative representation of the functioning of one system or process by the functioning of another”. This means that to determine how an actual system functions, you would build a model of the system and see how the model functions.

Simulations run in simulation time, an abstraction of real time. As the simulation clock advances, the model determines if there have been changes, recalculates its values, and outputs the results. If the model is valid, the outputs of the simulation will be reflective of the performance or behavior of the real system.

Simulation with ExtendSim means that instead of interacting with a real system you create a logical model that corresponds to the real system in certain aspects. You simulate the operations or dynamics of the system, then analyze one or more areas of interest. You do this in order to reduce risk and uncertainty so that you can make informed, timely decisions.

Modeling methodologies

The formalism you use to specify a system is termed a modeling methodology. The three main modeling methodologies are:

arrow Continuous
arrow Discrete event
arrow Discrete rate

In addition to these main modeling methodologies, other modeling approaches are useful and usually based on one of the three main methods:

arrow Monte Carlo
arrow Agent-based
arrow State/Action

As you might expect, you can use different methods to model different aspects of real-world systems. For example, at a chemical plant you could model the chemical reactions as a continuous process, the control logic of the chemical process using discrete event modeling, and the tanks, valves, and flow of the production process with discrete rate.

It is good to note, however, that there is no such thing as the model of a system: a system can be modeled in any number of different ways, depending on what it is you want to accomplish. In general, how you model the system depends on the purpose of the model: what type, level, and fidelity of information you want to gather and the amount of detail, or level of abstraction or granularity, of the model. Once that has been determined, you can intelligently choose which type of
model to build.

Comparison of main modeling methodologies

The three main modeling methodologies are continuous, discrete event, and discrete rate. Continuous modeling (sometimes known as process modeling) is used to describe a flow of values. Discrete event models track unique entities. Discrete rate models share some aspects of both continuous and discrete event modeling.

In all three types of simulations, what is of concern is the granularity of what is being modeled and what causes the state of the model to change.

arrow Continuous The time step is fixed at the beginning of the simulation, time advances in equal increments, and values change based directly on changes in time. In this type of model, values
reflect the state of the modeled system at any particular time, and simulated time advances evenly from one time step to the next. For example, an airplane flying on autopilot represents a continuous system since its state (such as position or velocity) changes continuously with respect to time. Continuous simulations are analogous to a constant stream of fluid passing through a pipe. The volume may increase or decrease at each time step, but the flow is continuous.
arrow Discrete event The system changes state as events occur and only when those events occur; the mere passing of time has no direct
effect on the model. Unlike a continuous model, simulated time advances from one event to the next and it is unlikely that the time between events will be equal. A factory that assembles parts is a good example of a discrete event system. The individual entities (parts) are assembled based on events (receipt or anticipation of orders). Using the pipe analogy for discrete event simulations, the pipe could be empty or have any number of separate buckets of water traveling through it. Rather than a continuous flow, buckets of water would come out of the pipe at random intervals.
arrow Discrete rate Discrete rate simulations are a hybrid type, combining aspects of continuous and discrete event modeling. Like continuous models they simulate the flow of stuff rather than items; like discrete event models they recalculate rates and values whenever events occur. Using the pipe analogy for a discrete rate simulation, there is a constant stream of fluid passing through the pipe. But the rates of flow and the routing can change when an event occurs.

These 3 main modeling types are compared and contrasted in the tables below.

In some branches of engineering, the term discrete is used to describe a system with periodic or constant time steps. Discrete, when it refers to time steps, indicates a continuous model; it does not have the same meaning as discrete event or discrete rate. Continuous models in ExtendSim are stepped using constant time intervals; discrete event and discrete rate models are not.

Other modeling approaches

Although there are several other approaches to modeling, they usually fit within one of the three major categories. For example, System Dynamics and Bond graphs are subsets of continuous modeling, and queuing theory models are
subsets of discrete event modeling.

Because of their specialized use, three specific modeling approaches (Monte Carlo, State/Action, and Agent Based) are described below.

arrow Monte Carlo

Widely used to solve certain problems in statistics, Monte Carlo simulations provide a range of results rather than a single value. This approach can be applied to any ExtendSim model and used wherever uncertainty is a factor.

Monte Carlo modeling uses random numbers to vary input parameters for a series of calculations. These calculations are performed many times and the results from each individual calculation are recorded as an observation. The individual observations are statistically summarized, giving an indication of the likely result and the range of possible results. This not only tells what could happen in a given situation, but how likely it is that it will happen.

You build a Monte Carlo simulation in ExtendSim by incorporating random elements in a model and obtaining multiple observations. There are two ways to do this:

The classical Monte Carlo method is to take a single mathematical equation or set of equations, then cause the equation to be calculated many times. In this type of simulation, time is not a factor. The entire model is run to completion and evaluated at each step; each subsequent step performs a new calculation.
An alternative Monte Carlo approach, typically applied in a discrete event model, is to either divide a single simulation run into multiple sections (batch means) or run the simulation many times (multirun analysis). Monte Carlo is incorporated by adding randomness to the model, running it many times, and analyzing the results. This method can be applied to any continuous, discrete event, or discrete rate model.
arrow Agent-based

With agent-based modeling you usually do not know model dynamics in advance; instead, you obtain that information from the interaction of the agents in the model.

Agent-based models share the following characteristics:

arrow The identification of individual entities within the model.
arrow A set of rules that govern individual behavior.
arrow The premise that local entities affect each other’s behavior.

Agent-based modeling is concerned with individual entities (called “agents”) that interact with other agents within their specified locality. All the agents have a set of rules to follow but they also have a degree of autonomy such that model dynamics cannot be predefined. This is because agents can have intelligence, memory, social interaction, contextual and spatial awareness, and the ability to learn.

arrow State/Action

With state/action modeling a system is modeled as a collection of discrete states. Sometimes known as a state chart, a state/action model represents a system that responds to an event by transitioning to another state. The model is composed of a series of states where each state depends on a previous state. A state has an associated action and an event that will cause that state to change to another. The transition from one state to the next is not sequential; each state can lead to any other state.

There are rules that govern the communication and transition between the states:

arrow All states accept events.
arrow One or more states may create an event as a result of a transition by another state or group of states.
arrow A group of states can be set to transition conditionally, for instance to only change if another state or group of states achieve a specific stage. These are known as guard conditions.


State/action models are independent of any of the three modeling methodologies (continuous, discrete event, or discrete rate.) They are useful for specification and verification in many areas, from computer programs to business processes.

Tables

Comparison Table

Modeling Method
ExtendSim library Value library
Electronics library
Item library Rate library
What is modeled Processes Individual items Flows of stuff
Examples Processes: chemical, biological, economic, electronic, geological. Things: traffic, equipment, work product, people.
Information: data, messages, and network protocols at the packet level.
Rate-based flows of stuff: homogeneous products (powders, fluids, oil, and gas), high-speed or high-volume production and packaging, data feeds and streams, mining.

Table of continuous, discrete event, and discrete rate differences

Use this table as a guide to help determine which style to use when modeling a system.

Factor

Continuous

Discrete event

Discrete rate

What is modeled

Values that flow through the model.

Distinct entities ("items" or "things").

Bulk flows of homogeneous stuff. Or flows of otherwise distinct entities where sorting or separating is not necessary.

What causes a change in state

A time change

An event

An event

Time steps

Interval between time steps is constant. Model recalculations are sequential and time dependent.

Interval between events is dependent on when events occur. Model only recalculates when events occur.

Interval between events is dependent on when events occur. Model only recalculates when events occur.
Characteristics of what is modeled Track characteristics in a database or assume the flow is homogeneous. Using attributes, items are assigned unique characteristics and can then be tracked throughout the model. Track characteristics in a database or assume the flow is homogeneous.

Ordering

FIFO

Items can move in FIFO, LIFO, Priority, time-delayed, or customized order.

FIFO

Routing

Values need to be explicitly routed by being turned off at one branch and turned on at the other (values can go to multiple places at the same time).

By default, items are automatically routed to the first available branch (items can only be in one place at a time).

Flow is routed based on constraint rates and rules that are defined in the model (flow can be divided into multiple branches).

Statistical detail

General statistics about the system: amount, efficiency, etc.

In addition to general statistics, each item can be individually tracked: count, utilization, cycle time.

In addition to general statistics, effective rates, cumulative amount.

Typical uses

Scientific (biology, chemistry, physics), engineering (electronics, control systems), finance and economics, System Dynamics.

Manufacturing, service industries, business operations, networks, systems engineering.

Manufacturing of powders, fluids, and high speed, high volume processes. Chemical processes, ATM transactions. Supply chains. 
Recommended package ExtendSim CP ExtendSim OR, ExtendSim AT, or ExtendSim Suite ExtendSim AT or ExtendSim Suite
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