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QUEUING THEORY

QUEUING THEORY. Made by Abdulsalam shek salim. 17700685. Introduction. Body of knowledge about waiting lines Helps managers to better understand systems in manufacturing, service, and maintenance Provides competitive advantage and cost saving. A QUEUE REPRESENTS ITEMS

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QUEUING THEORY

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  1. QUEUING THEORY Made by Abdulsalam shek salim 17700685

  2. Introduction • Body of knowledge about waiting lines • Helps managers to better understand systems in manufacturing, service, and maintenance • Provides competitive advantage and cost saving A QUEUE REPRESENTS ITEMS OR PEOPLE AWAITING SERVICE

  3. Queue Characteristics 1) Average number of customers in a line 2) Average number of customers in a service facility 3) Probability a customer must wait 4) Average time a customer spends in a waiting line. 5) Average time a customer spends in a service facility 6) Percentage of time a service facility is busy

  4. Queuing System Examples

  5. The Father of Queuing Theory Danish engineer, who, in 1909 experimented with fluctuating demand in telephone traffic in Copenhagen. In 1917, he published a report addressing the delays in auto-matic telephone dialing equip-ment. At the end of World War II, his work was extended to more general problems, including waiting lines in business. AGNER K. ERLANG

  6. Lack of Managerial Intuition Surrounding Waiting Lines • Queuing theory is not a matter of common sense. It is one of those applications where diligent, intelligent managers will arrive at drastically wrong solutions if they fail to thoroughly appreciate and understand the mathematics involved.

  7. THE QUEUING COST TRADE-OFF Cost Total Cost Cost of Providing Service ( salaries + benefits ) Minimum Total Cost Cost of Waiting Time ( time x value of time ) Low Level Of Service Optimal Service Level High Level Of Service

  8. Aspects of a Queuing Process • SYSTEM ARRIVALS • THE QUEUE ITSELF • THE SERVICE FACILITY

  9. Poisson Arrival Distribution P ( probability ) .25 .20 .15 .10 .05 .00 Poisson Probability Distribution for λ = 2 (estimated mean arrival rate) 0 1 2 3 4 5 6 7 8 9 10 X ( the number of arrivals )

  10. Poisson Arrival Distribution P ( probability ) .25 .20 .15 .10 .05 .00 Poisson Probability Distribution for λ = 4 (estimated mean arrival rate) 0 1 2 3 4 5 6 7 8 9 10 X ( the number of arrivals )

  11. Establishing A Discrete Poisson Arrival Distribution Given any average arrival rate ( λ ) in seconds, minutes, hours, days: - λ x P ( X ) = ελ X! ( FOR X = 0,1,2,3,4,5, etc. ) Where : P ( X ) = probability of X arrivals X = number of arrivals per time unit λ = the average arrival rate ε = 2.7183 ( base of the natural logarithm )

  12. EXAMPLE If the average arrival rate per hour is two people ( λ = 2 ) , what is the probability of three ( 3 ) arrivals per hour?

  13. Solution Working on the same formula which we explain it befor .. X - λ λ ε P ( X ) = X ! - 2 3 Given λ = 2 : P ( 3 ) = 2.7183 2 3 ! =[ 1 / 7.389 ] x 8 (3)(2)(1) = .1353 x 8 = .1804 ≈ 18% 6

  14. Precise Terminology Theoretical Distribution Observed Distribution The discrete arrival probability distribution, based on the average arrival rate ( λ ) which was computed from the actual system observations. The actual discrete arrival probability distribution that was constructed from the actual system observations. THIS DISTRIBUTION MAY OR MAY NOT BE POISSON DISTRIBUTED.

  15. Service Times Service times normally follow a negative exponential probability distribution .25 .20 .15 .10 .05 .00 P R O B A B I L I T Y THE PROBABILITY A CUSTOMER WILL REQUIRE THAT SERVICE TIME 0 30 60 90 120 150 180 210 seconds

  16. Some of Queuing Theory Variables • Lambda ( λ ) is the average arrival rate of people or items into the service system. • It can be expressed in seconds, minutes, hours, or days. • From the Greek small letter “ L “.

  17. Queuing Theory Variables • Mu ( μ ) is the average service rate of the service system. • It can be expressed as the number of people or items processed per second, minute, hour, or day. • From the Greek small letter “ M “.

  18. Queuing Theory Variables • Rho ( ρ ) is the % of time that the service facility is busy on the average. • It is also known as the utilization rate. • From the Greek small letter “ R “.

  19. Queuing Theory Variables • Mu ( M ) is a channel or service point in the ser-vice system. • Examples are gasoline pumps, checkout coun-ters, vending machines, bank teller windows. • From the Greek large letter “ M “.

  20. IMPORTANT CONSIDERATION • The average service rate must always exceed the average arrival rate. • Otherwise, the queue will grow to infinity. μ > λ THERE WOULD BE NO SOLUTION !

  21. Dual-Channel / Single-Phase System EXIT • ONE WAITING LINE or QUEUE • ONE SERVICE POINT or CHANNEL

  22. Dual-Channel / Single-Phase System EXIT EXIT No Jockeying Permitted Between Lines • ONE OR TWO WAITING LINES • TWO DUPLICATE SERVICE POINTS

  23. Dual-Channel / Single-Phase System EXIT EXIT Jockeying Is Permitted Between Lines ! ENTER ENTER • TWO IDENTICAL SERVICE CHANNELS. • EACH CHANNEL HAS 3 DISTINCT SERVICE POINTS ( A-B-C )

  24. Thank you all for listening We have too much things to speak about Queuing Theory but because of the we are we are competing with the time it will be enough until here .

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