Understanding Simulation

• Simulation is derived from the Latin "simulare", meaning "to pretend"
• Simulation is a safe experimentation method of a system model and a powerful problem solving technique
• The quality of the system model affects simulation results
• Simulation involves manipulating a system model in time/space to reveal relationships and interactions
• Simulation helps in understanding problems where errors/faults are unacceptable
• Simulation computerizes a developed model to study part interactions
• System simulation development is iterative
• Simulation is an operational research tool to improve system performance, processes and facility planning
• It bridges exact analysis and physical intuition

Advantages

• Deeper system understanding

• Better insight into resource performance

• Enables stakeholders to test alternatives

• Saves time and money

• Can generate virtual environments

• Improves analysis quality

• Complex system performance is hard to predict due to system complexity or insufficient core theory

• Conventional mathematical modeling struggles with uncertainties, dynamic interactions and variable interdependencies

Types of Simulation

• Physical Simulation deals with experimenting on a physical prototype, comparing real scaled input and system output
• Numerical Simulation is a common simulation type aligned with the systems approach
• Numerical simulation of mathematical models is popular as they are easy to develop and modify
• It is considered a sequential simulation because calculations are time-based
• Digital (Computer) Simulation uses digital computers to simulate equations describing a system
• Digital simulation is marked by representation clarity and high automation

Characteristics of Simulation

Efficiency

• Higher mission availability
• Increased operational system availability
• Transportation avoidance
• Reduced or eliminated expendable costs
• Less procurement and operational costs

Effectiveness

• Improved proficiency or performance
• Fewer complex activities
• Neutral and obstruction services
• Greater observation, assessment, and/or analysis capability

Risk Reduction

• Safety of stakeholders
• Less environmental impact
• Machinery and equipment handling

Simulation Techniques

Monte Carlo Simulation

• Mathematical simulation for generating random sample data from a known distribution for numerical experiments

• Applied to quantitative risk analysis and decision-making

• Used when necessary and uncertain decisions are inevitable and can be used for stochastic and deterministic problems

• A typical Monte Carlo simulation calculates a model hundreds/thousands of times using different, randomly selected values

• The results are used to describe the probability of reaching various results in the simulation model

Characteristics

• Output must generate random samples
• Must know the input distribution
• Result must be known while performing an experiment

Monte Carlo Simulation Example

• Estimate the time to complete a three-part construction project by summing the time for each part

• A range of possible values creates a realistic picture

• With min/max estimates for each part, those values can be used to estimate total minimum and maximum time

• To test the likelihood of a particular result, count how many times the model returned that particular result in the simulation

• The model in Table 3 demonstrates risks and different choices impact project development

• Knowing risks at the beginning can lead to more accurate results and better planning

Agent-Based Simulation

• Focuses on individual components
• Active entities (people, vehicles, equipment, products) must be identified with defined behaviors
• Connections between entities must be established, environmental variables are set

Agent-Based Simulation Benefits

• Use when interactions are complex, nonlinear, or discontinuous

• Use when space is vital and positions are not fixed

• Use when the population is heterogeneous and each individual is potentially different

• Crowd stampedes often lead to fatalities

• Agent-based simulation can indicate the pillar regulates the outflow of people leading to fewer casualties

Discrete Event Simulation

• Focuses on processes in a system at a medium level of abstraction
• Used in manufacturing, logistics, and healthcare
• Codifies system behavior with ordered events
• An event comprises a specific change in the system's state at a specific point in time

Characteristics of Discrete Event Simulation

• Predetermined starting and ending points

• A list of discrete events that have occurred

• A list of discrete events which are pending or incoming

• Tracks the time that has elapsed since the process began

• A record of the function in use

• In discrete system simulation, the following may be considered:

  • System states: Q(t) – number of customers in line; S(t) – server status at time t
  • Entities: customer and server
  • Events: A – arrival; D – departure; E – stopping event
  • Event Notice: (A, t) – an arrival event to occur at time t; (D, t) – a customer departure at time t (E, Tsim) – the simulation stop event at time Tsim
  • Activities: inter-arrival time, service time
  • Delay: time spent of each customer in the system

System Dynamics Simulation

• Highly abstracted method of modeling and simulation

• Ignores fine details of a system (people, products, events)

• Produces a general representation of a complex system

• Abstract simulation models may be used for long-term strategic modeling and simulation

• A 3D model snippet from a software system dynamics simulator is a good example

• Calculated values are tied to rod and piston parameters

• Piston provides force for wheel rotation

• Affected by the speed of rotation, the radius of the wheel, and the length of the rod