BUSOBA 2321 FINAL UPDATED ACTUAL EXAM QUESTIONS AND
DETAILED CORRECT ANSWERS
Analogue (performance) simulation
- product design and testing
- space walks
- "games"
Monte carlo (risk) simulation
- models the uncertainty or randomness of a system by replicating it
many times with different values for random inputs
- provides knowledge of the underlying distribution of the uncertain
events and a better understanding of the distribution of possible
outcomes and the risk involved
- two step process (formulate and input)
System (performance) modeling
- typically used to analyze system performance and the effects of
changes on system performance
- continuous systems (weather, wind flow)
- discrete systems (production, logistics, transportation, service)
Simulation advantages
,- straightforward and flexible
- can analyze large and complex real-world situations
- allows the user to ask "what if"
- do not interfere with real-world systems
- identifies important component through simulation interactions
- time compression is possible
- allows the inclusion of real-world complications
Simulation disadvantages
- can be expensive and time-consuming
- does not generate optimal solutions, but runs trial and error
approach yielding different results with each run
- requires the generation of all conditions and constraints
- each model is unique, not transferable to other problems
Random number
=rand()
Continuous uniform distribution
=a+(b-a)*rand()
, Discrete uniform distribution
=int(a+(b-a)*rand()) OR =randbetween(a,b)
Normal distribution
=norminv(rand(),mu,sigma)
Exponential distribution
=-(1/mu)*ln(rand)))
Discrete general distribution
=if(rand(),range1,range2)
Three phases of project management
- planning
- scheduling
- controlling
An unbounded solution means that
As one or more variables increases the objective function value
becomes more optimal
DETAILED CORRECT ANSWERS
Analogue (performance) simulation
- product design and testing
- space walks
- "games"
Monte carlo (risk) simulation
- models the uncertainty or randomness of a system by replicating it
many times with different values for random inputs
- provides knowledge of the underlying distribution of the uncertain
events and a better understanding of the distribution of possible
outcomes and the risk involved
- two step process (formulate and input)
System (performance) modeling
- typically used to analyze system performance and the effects of
changes on system performance
- continuous systems (weather, wind flow)
- discrete systems (production, logistics, transportation, service)
Simulation advantages
,- straightforward and flexible
- can analyze large and complex real-world situations
- allows the user to ask "what if"
- do not interfere with real-world systems
- identifies important component through simulation interactions
- time compression is possible
- allows the inclusion of real-world complications
Simulation disadvantages
- can be expensive and time-consuming
- does not generate optimal solutions, but runs trial and error
approach yielding different results with each run
- requires the generation of all conditions and constraints
- each model is unique, not transferable to other problems
Random number
=rand()
Continuous uniform distribution
=a+(b-a)*rand()
, Discrete uniform distribution
=int(a+(b-a)*rand()) OR =randbetween(a,b)
Normal distribution
=norminv(rand(),mu,sigma)
Exponential distribution
=-(1/mu)*ln(rand)))
Discrete general distribution
=if(rand(),range1,range2)
Three phases of project management
- planning
- scheduling
- controlling
An unbounded solution means that
As one or more variables increases the objective function value
becomes more optimal
