ISYE 6644 - M1 (Modules 1 - 5.L9 ACTUAL UPDATED QUESTIONS AND CORRECT
ANSWERS
Steps in a Simulation Study The iterative process involved in carrying out a simulation, including problem
formulation, objectives and planning, model building, data collection, coding,
verification, model validation, experimental design, running experiments, output
analysis, and reporting/implementation.
Problem Formulation The initial statement of the problem, such as "Profits are too low" or "Customers
are complaining about the long lines".
Objectives and Planning Identifying specific questions to answer, such as "How many workers to hire?" or
"How much buffer space to insert in the assembly line?".
Model Building The process of creating an abstract representation of the system, involving both
art and science, potentially using mathematical models like M/M/k queuing or
physics equations.
Data Collection Determining the types and amount of data needed, considering continuous or
discrete data, what to collect, and budget considerations.
Coding Deciding on a simulation language or modeling paradigm (like Event-Scheduling
or Process-Interaction) and writing the simulation program.
Verification Checking if the code is correct and free of obvious programming errors. If not,
returning to the coding step. This is a programming issue.
Validation Checking if the model accurately represents the real system, often using statistical
techniques. If the model is not valid, returning to model building and data
collection.
Experimental Design Determining the experiments to run efficiently to answer the study's questions,
considering statistical requirements and time/budget constraints.
Run Experiments Executing the simulation program for production runs, which can be substantial
and require significant time.
Output Analysis Performing statistical analysis on the results of the experiments to estimate
relevant measures of performance and calculate confidence intervals. This is often
an iterative process with experimental design and production runs, and typically
requires more runs.
Make Reports, Implement, and Make Management The final steps of documenting the results, putting the findings into practice if
Happy possible, and ensuring the outcome satisfies stakeholders.
, System A collection of entities that interact together to accomplish a goal.
Model An abstract representation of a system, usually containing mathematical or logical
relationships describing the system in terms of states, entities, sets, events, etc..
System state A set of variables that contains enough information to describe the system at any
point in time, acting as a "snapshot". For example, in a single-server queue, this
could be the number of people in the queue at time t, and whether the server is
busy or idle at time t.
Entities Things in the system that interact, such as people (e.g., customers) or machines.
They can be permanent (like a machine) or temporary (like customers).
Attributes Properties or characteristics of entities, such as the priority of a customer or the
average speed of a server.
List (or queue) An ordered list of associated entities, such as a line of people waiting for service.
Event A point in time at which the system state changes, and which generally can't be
predicted with certainty beforehand. Examples include an arrival event, a
departure event, or a machine breakdown event. Loosely, it can also refer to
"what" happens. In discrete-event simulation, the clock moves from event to event.
Activity A duration of time of specified length, also known as an unconditional wait.
Examples include exponential customer interarrival times or constant service
times, which are explicitly generated and thus "specified".
Unconditional wait Another term for an activity, representing a duration of time of specified length.
ANSWERS
Steps in a Simulation Study The iterative process involved in carrying out a simulation, including problem
formulation, objectives and planning, model building, data collection, coding,
verification, model validation, experimental design, running experiments, output
analysis, and reporting/implementation.
Problem Formulation The initial statement of the problem, such as "Profits are too low" or "Customers
are complaining about the long lines".
Objectives and Planning Identifying specific questions to answer, such as "How many workers to hire?" or
"How much buffer space to insert in the assembly line?".
Model Building The process of creating an abstract representation of the system, involving both
art and science, potentially using mathematical models like M/M/k queuing or
physics equations.
Data Collection Determining the types and amount of data needed, considering continuous or
discrete data, what to collect, and budget considerations.
Coding Deciding on a simulation language or modeling paradigm (like Event-Scheduling
or Process-Interaction) and writing the simulation program.
Verification Checking if the code is correct and free of obvious programming errors. If not,
returning to the coding step. This is a programming issue.
Validation Checking if the model accurately represents the real system, often using statistical
techniques. If the model is not valid, returning to model building and data
collection.
Experimental Design Determining the experiments to run efficiently to answer the study's questions,
considering statistical requirements and time/budget constraints.
Run Experiments Executing the simulation program for production runs, which can be substantial
and require significant time.
Output Analysis Performing statistical analysis on the results of the experiments to estimate
relevant measures of performance and calculate confidence intervals. This is often
an iterative process with experimental design and production runs, and typically
requires more runs.
Make Reports, Implement, and Make Management The final steps of documenting the results, putting the findings into practice if
Happy possible, and ensuring the outcome satisfies stakeholders.
, System A collection of entities that interact together to accomplish a goal.
Model An abstract representation of a system, usually containing mathematical or logical
relationships describing the system in terms of states, entities, sets, events, etc..
System state A set of variables that contains enough information to describe the system at any
point in time, acting as a "snapshot". For example, in a single-server queue, this
could be the number of people in the queue at time t, and whether the server is
busy or idle at time t.
Entities Things in the system that interact, such as people (e.g., customers) or machines.
They can be permanent (like a machine) or temporary (like customers).
Attributes Properties or characteristics of entities, such as the priority of a customer or the
average speed of a server.
List (or queue) An ordered list of associated entities, such as a line of people waiting for service.
Event A point in time at which the system state changes, and which generally can't be
predicted with certainty beforehand. Examples include an arrival event, a
departure event, or a machine breakdown event. Loosely, it can also refer to
"what" happens. In discrete-event simulation, the clock moves from event to event.
Activity A duration of time of specified length, also known as an unconditional wait.
Examples include exponential customer interarrival times or constant service
times, which are explicitly generated and thus "specified".
Unconditional wait Another term for an activity, representing a duration of time of specified length.
