WOOD 492 MODELLING FOR DECISION SUPPORT
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Transcript of WOOD 492 MODELLING FOR DECISION SUPPORT
WOOD 492 MODELLING FOR DECISION SUPPORT
Lecture 25
Simulation
Wood 492 - Saba Vahid 2
Review
• Simulation: used to imitate the real system using computer software, helpful when system is too complex or has many stochastic elements
• Discrete event simulation: if the state of the system changes at random points in time as a result of various events
• Different probability distributions are used for different purposes
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Distributions
• Various probability distributions are used for different random events
• Poisson : distribution of number of arrivals per unit of time
• Exponential : distribution of time between successive events (arrivals, serving customers,…)
• Uniform: for random number generation
• Normal : for some physical phenomenon's, normally used to represent the distributions of the means of observations from other distributions
• Binomial: coin flip
• …
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Cumulative Distribution Function (CDF)
• CDF is calculated using the area under the probability density graph (PDF):
• Assume x is a random variable and t is a possible value for x. If we show the PDF of x with f(x) and the CDF with F(x):
=(the area under f(x) up to point t)
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f(x)
F(x)
x
x
1.0
t
t
Highlighted area: P(x<=t)
P(x<=t)
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Example 16 – A discrete event simulation
• Simulate a queuing system :– One server– Customers arrive according to a Poisson distribution (mean arrival
rate λ = 3 per hour)– Service rate changes according to a Poisson distribution (mean
service rate μ = 5 customers per hour)
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InputSource Queue Server
ServedCustomers
Arrival rate (Inter-arrival Time)
Service Rate(Service times)
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Probability reminder
1. When the arrival rate α (number of arrivals per unit of time t) follows a Poisson distribution with the mean of αt, it means that inter-arrival times (the time between each consecutive pair of arrival) follow an exponential distribution with the mean of 1/α
2. If x belongs to an Exponential distribution with the mean 1/α:
3. Therefore, if customers arrive with the mean rate of 3 per hour, the inter-arrival time has an exponential distribution with the mean of 1/3 hour (on average one arrival happens every 1/3 hour)
So, for example, the probability of an arrival happening in the first hour (time of event, x, is less than or equal to 1 hour, t)
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Example 16 – Queuing system
• State of the system at each time t– N(t) = number of customers in the queue at time t
• Random events in the simulation:– Arrival of customers (mean inter arrival times are 1/3 hour)– Serving the customers (mean service times are 1/5 hour)
• System transition formula:– Arrival: reset N(t) to N(t)+1– Serve customer: reset N(t) to N(t)-1
• How to change the simulation clock (2 ways):1. Fixed-time increment
2. Next-event increment
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Fixed-time increment for Example 16
• Two steps:
1. Advance the clock by a small fixed amount (e.g: 0.1 hour)
2. Update N(t) based on the events that have occurred (arrivals and serving customers)
Example: let’s move the clock from t=0 to t=0.1 hr
N(0)=0
Probability of an arrival happening in the first 0.1 hr is:
Probability of a departure happening in the first 0.1 hr is:
How to use these probabilities?
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Using random numbers to generate events
• To see if the events should occur or not, we use a random number generator to generate a uniform random number between [0,1] (e.g. in Excel there is a RAND() function that does this)
• If the random number is less than the calculated probability (in previous slide) we accept the event, if not we reject it.• let’s assume we’ve generated a random number for the arrival of
customers with Rand() function, random_A=0.1351
Random_A < 0.259 so we accept the arrival
We must generate a new random number for each case, so let’s assume random_D=0.5622
Random_D >= 0.393 so we reject the departure
N(1) = N(0)+ 1 (arrival) – 0 (departure) = 0+1=1
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Example 16