1. The Theory of Queues Models of Waiting in line

2. Queuing Theory Basic model: Arrivals  Queue  Being Served  Done – Queuing theory lets you calculate: How long will it take an average customer to get done? How many customers are waiting, on average?

3. Why this is useful How long will it take a customer to get done? – Time allocation For customers For service How many customers are waiting? – Space allocation – Social dynamic

4. Arrivals  Queue  Being Served  Done – Jargon: Queue < System Queue is a subset of the System System = In queue + being served

5. Arrivals  Queue  Being Served  Done – Standard Greek letters: λ (Lambda) – Average arrival rate μ (Mu) – Average service rate 1/λ = Average time between arrivals 1/μ = Average time for service

6. Steady State -- when average performance is not changing λ Lambda – Average arrival rate μ Mu – Average service rate If μ > λ – if the service rate is faster than the arrival rate – there is a steady state. If λ > μ, there is no steady state. The queue will grow and grow.

7. Y a Q if λ < μ? Why is there a queue if customers are served faster than they are arriving, on average?

8. Y a Q if λ<μ? Why is there a queue if customers are served faster than they are arriving, on average? Because of random variation in arrival and service times.

9. Randomness Modeled – Suppose … An event can happen at any time Events are independent We know the average rate of occurrence – Then we use … Poisson distribution – random variation with no limit – extremes are unlikely but possible

10. Poisson distribution Limit of the binomial Cut t amount of time into smaller and smaller pieces Only one arrival allowed during each piece. The probability of an arrival during the piece is λ/N, where N is the number of pieces. As N  ∞, the probability of x arrivals during t amount of time is …

11. Poisson distribution Prob (x arrivals during time t) =

17. 10 pieces

19. A model of randomness and independence of events – Using the Poisson distribution in your queuing model means adopting to binomial distribution’s assumptions: An event can happen at any time With equal probability Independent events

20. Poisson distribution application Running total of Poisson distribution can show how much inventory is needed.

21. Exponential Distribution Probability that the next ARRIVAL will be during the next t amount of time =

22. Exponential Distribution Probability that the SERVER will finish with the current customer during the next t amount of time =

23. Exponential Distribution example If μ = 1 per hour, Probability the the SERVER will finish current customer during the next hour is 1 – e -1 = 0.632 For half of the customers, the service takes less than 42 minutes.

24. Steady State When the distribution of probabilities of various numbers in the system stabilizes When startup conditions are far behind λ<μ required for steady state to exist ρ=λ/μ is how busy the server is ρ is server busy time ÷ all time ρ< 1 for steady state to be possible

25. Steady State with Poisson arrivals and service times For single-server single-stage queue with Poisson arrivals and service: Probability of n in the system (waiting or being served)= (1-ρ)ρ n

26. Customers in System and in Queue L – customers in system = L q – customers in queue = ρL These are averages, once the steady state is reached. There are usually fewer in the system than L because the prob of n in system is skewed.

27. L = ρ/(1-ρ) = λ/(u-λ) When the arrival rate nearly equals the service rate, the line can get very long. If the arrival rate nearly equals the service rate, small changes in either rate can make a big difference.

28. Time in System W – time in system = L/λ W q – time waiting to be served = ρW These are averages, once the steady state is reached. Most customers get through in less time.

29. W = L/λ = 1/(u-λ) When the arrival rate and the service rate are nearly equal, the wait can be very long. When the arrival rate and the service rate are nearly equal, small changes in either rate can make a big difference.

30. Economic Analysis Weigh The cost of waiting – Against -- The cost of speeding up service or reducing arrivals.