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Queuing Theory

Queuing Theory. Introduction. Queuing is the study of waiting lines, or queues. The objective of queuing analysis is to design systems that enable organizations to perform optimally according to some criterion. Possible Criteria Maximum Profits. Desired Service Level.

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Queuing Theory

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  1. Queuing Theory

  2. Introduction • Queuing is the study of waiting lines, or queues. • The objective of queuing analysis is to design systems that enable organizations to perform optimally according to some criterion. • Possible Criteria • Maximum Profits. • Desired Service Level.

  3. Analyzing queuing systems requires a clear understanding of the appropriate service measurement. • Possible service measurements • Average time a customer spends in line. • Average length of the waiting line. • The probability that an arriving customer must wait for service.

  4. The Arrival Process • There are two possible types of arrival processes • Deterministic arrival process. • Random arrival process. • The random process is more common in businesses. • A Poisson distribution can describe the random arrival process.

  5. Types of Queuing Systems Single Channel Single Phase: Trucks unloading shipments into a dock.

  6. Types of Queuing Systems Single Line Multiple Phase: Wendy’s Drive Thru -> Order + Pay/Pickup

  7. Types of Queuing Systems Multiple Line Single Phase: Walgreens Drive-Thru Pharmacy

  8. Types of Queuing Systems Multiple Line Multiple Phase: Hospital Outpatient Clinic, Multi-specialty

  9. Fundamentals of Queuing Theory • Microscopic traffic flow • Arrivals • Uniform or random • Departures • Uniform or random • Service rate • Departure channels • Discipline • FIFO and LIFO are most popular • FIFO is more prevalent in traffic engineering

  10. Poisson Distribution • Count distribution • Uses discrete values • Different than a continuous distribution

  11. Poisson Distribution • Video

  12. Poisson Ideas • Probability of exactly 4 vehicles arriving • P(n=4) • Probability of less than 4 vehicles arriving • P(n<4) = P(0) + P(1) + P(2) + P(3) • Probability of 4 or more vehicles arriving • P(n≥4) = 1 – P(n<4) = 1 - P(0) + P(1) + P(2) + P(3) • Amount of time between arrival of successive vehicles

  13. Poisson Distribution ExampleGroup Activity Vehicle arrivals at the Olympic National Park main gate are assumed Poisson distributed with an average arrival rate of 1 vehicle every 5 minutes. What is the probability of the following: • Exactly 2 vehicles arrive in a 15 minute interval? • Less than 2 vehicles arrive in a 15 minute interval? • More than 2 vehicles arrive in a 15 minute interval?

  14. Example Calculations Exactly 2: Less than 2: More than 2:

  15. - l t k ( l t) e P ( X = k ) = k ! The Poisson Arrival Distribution Where l = mean arrival rate per time unit. t = the length of the interval. e = 2.7182818 (the base of the natural logarithm). k! = k (k -1) (k -2) (k -3) … (3) (2) (1).

  16. The Waiting Line • Line configuration • A Single service Queue. • Multiple service queue with single waiting line. • Multiple service queue with multiple waiting lines. • Tandem queue (multistage service system). • Jockeying • Jockeying occurs if customers switch lines when they perceived that another line is moving faster. • Balking • Balking occurs if customers avoid joining the line when they perceive the line to be too long.

  17. Priority rules • Priority rules define the line discipline. • These rules select the next customer for service. • There are several commonly used rules: • First come first served (FCFS). • Last come first served (LCFS). • Estimated service time. • Random selection of customers for service. • Homogeneity • An homogeneous customer population is one in which customers require essentially the same type of service. • A Nonhomogeneous customer population is one in which customers can be categorized according to: • Different arrival patterns • Different service treatments.

  18. The Service Process • Some service systems require a fixed service time. • In most business situations, however, service time varies widely among customers. • When service time varies, it is treated as a random variable. • The exponential probability distribution is used sometimes to model customer service time.

  19. f(X) = me-mX The probability that the service time X is less than some “t.” P(X t) = 1 - e-mt • The Exponential Service Time Distribution where m = is the average number of customers who can be served per time period.

  20. Schematic illustration of the exponential distribution f(X) The probability that service is completed within t time units X = t

  21. Measures of Queuing System Performance • Performance can be measured by focusing on: • Customers in queue. • Customers in the system. • Transient and steady state periods complicate the service time analysis

  22. The transient period occurs at the initial time of operation. • Initial transient behavior is not indicative of long run performance. • The steady state period follows the transient period. • In steady state, long run probabilities of having “n” customers in the system do not change as time goes on. • In order to achieve steady state, the effective arrival rate must be less than the sum of effective service rates . l< m l< m1 +m2+…+mk l< km For one server For k servers For k servers each with service rate m

  23. Steady State Performance Measures P0 = Probability that there are no customers in the system. Pn = Probability that there are “n” customers in the system. L = Average number of customers in the system. Lq = Average number of customers in the queue. W = Average time a customer spends in the system. Wq = Average time a customer spends in the queue. Pw = Probability that an arriving customer must wait for service. r = Utilization rate for each server (the percentage of time that each server is busy).

  24. Little’s Formulas • Little’s Formulas represent important relationships between L, Lq, W, and Wq. • These formulas apply to systems that meet the following conditions: • Single queue systems, • Customers arrive at a finite arrival rate l, and • The system operates under steady state condition. L = l W Lq = lWqL = Lq + l / m For the infinite population case

  25. Queue Notation • Popular notations: • D/D/1, M/D/1, M/M/1, M/M/N • D = deterministic distribution • M = exponential distribution Number of service channels Arrival rate nature Departure rate nature

  26. Queuing Theory Applications • D/D/1 • Use only when absolutely sure that both arrivals and departures are deterministic • M/D/1 • Controls unaffected by neighboring controls • M/M/1 or M/M/N • General case • Factors that could affect your analysis: • Neighboring system (system of signals) • Time-dependent variations in arrivals and departures • Peak hour effects in traffic volumes, human service rate changes • Breakdown in discipline • People jumping queues! More than one vehicle in a lane! • Time-dependent service channel variations • Grocery store counter lines

  27. Total vehicle delay Queue Analysis – Graphical D/D/1 Queue Departure Rate Delay of nth arriving vehicle Arrival Rate Maximum queue Vehicles Maximum delay Queue at time, t1 t1 Time

  28. Queue Analysis – Numerical • M/D/1 • Average length of queue • Average time waiting in queue • Average time spent in system λ = arrival rate μ = departure rate

  29. Queue Analysis – Numerical • M/M/1 • Average length of queue • Average time waiting in queue • Average time spent in system λ = arrival rate μ = departure rate

  30. Performance Measures for the M / M /1 Queue P0 = 1- (l/m) Pn = [1 - (l/m)] (l/m)n L = l/(m - l) Lq = l2/ [m(m - l)] W = 1 / (m - l) Wq = l/ [m(m - l)] Pw = l/m r = l/m The probability that a customer waits in the system more than “t” is P(X>t)= e-(m - l)t

  31. Queue Analysis – Numerical • M/M/N • Average length of queue • Average time waiting in queue • Average time spent in system λ = arrival rate μ = departure rate

  32. M/M/N – More Stuff • Probability of having no vehicles • Probability of having n vehicles • Probability of being in a queue λ = arrival rate μ = departure rate

  33. Example 1 You are entering a Club to watch a basketball game. There is only one ticket line to purchase tickets. Each ticket purchase takes an average of 18 seconds. The average arrival rate is 3 persons/minute. Find the average length of queue and average waiting time in queue assuming M/M/1 queuing.

  34. Solution • Departure rate: μ = 18 seconds/person or 3.33 persons/minute • Arrival rate: λ = 3 persons/minute • ρ = 3/3.33 = 0.90 • Q-bar = 0.902/(1-0.90) = 8.1 people • W-bar = 3/3.33(3.33-3) = 2.73 minutes • T-bar = 1/(3.33 – 3) = 3.03 minutes

  35. Example 2 You are now in line to get into the Arena. There are 3 operating turnstiles with one ticket-taker each. On average it takes 3 seconds for a ticket-taker to process your ticket and allow entry. The average arrival rate is 40 persons/minute. Find the average length of queue, average waiting time in queue assuming M/M/N queuing. What is the probability of having exactly 5 people in the system?

  36. Solution • N = 3 • Departure rate: μ = 3 seconds/person or 20 persons/minute • Arrival rate: λ = 40 persons/minute • ρ = 40/20 = 2.0 • ρ/N = 2.0/3 = 0.667 < 1 so we can use the other equations • P0 = 1/(20/0! + 21/1! + 22/2! + 23/3!(1-2/3)) = 0.1111 • Q-bar = (0.1111)(24)/(3!*3)*(1/(1 – 2/3)2) = 0.88 people • T-bar = (2 + 0.88)/40 = 0.072 minutes = 4.32 seconds • W-bar = 0.072 – 1/20 = 0.022 minutes = 1.32 seconds • Since n > N (5 > 3) • Pn = 25(0.1111)/(35-3*3!) = 0.0658 = 6.58%

  37. Example 3 You are now inside the Arena. They are passing out Harry the Husky doggy bags as a free giveaway. There is only one person passing these out and a line has formed behind her. It takes her exactly 6 seconds to hand out a doggy bag and the arrival rate averages 9 people/minute. Find the average length of queue, average waiting time in queue, and average time spent in the system assuming M/D/1 queuing.

  38. Solution • N = 1 • Departure rate: μ = 6 seconds/person or 10 persons/minute • Arrival rate: λ = 9 persons/minute • ρ = 9/10 = 0.9 • Q-bar = (0.9)2/(2(1 – 0.9)) = 4.05 people • W-bar = 0.9/(2(10)(1 – 0.9)) = 0.45 minutes = 27 seconds • T-bar = (2 – 0.9)/((2(10)(1 – 0.9) = 0.55 minutes = 33 seconds

  39. MARY’s SHOES • Customers arrive at Mary’s Shoes every 12 minutes on the average, according to a Poisson process. • Service time is exponentially distributed with an average of 8 minutes per customer. • Management is interested in determining the performance measures for this service system.

  40. Pw = l/m = 0.6667 r = l/m = 0.6667 SOLUTION • Input l = 1/ 12 customers per minute = 60/ 12 = 5 per hour. m = 1/ 8 customers per minute = 60/ 8 = 7.5 per hour. • Performance Calculations • P0 = 1- (l/m) = 1 - (5 / 7.5) = 0.3333 • Pn = [1 - (l/m)] (l/m) = (0.3333)(0.6667)n • L = l/ (m - l) = 2 • Lq = l2/ [m(m - l)] = 1.3333 • W = 1 / (m - l) = 0.4 hours = 24 minutes • Wq = l/ [m(m - l)] = 0.26667 hours = 16 minutes

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