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Queuing Theory General Introduction

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This gives you a good overview of Queuing theory principles and can be used for any Production and Operations Designer.
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Queueing Theory and its Applications A Personal View ICAI 2010, Eger, Hungary 27 30 January, 2010 János Sztrik University of Debrecen, Debrecen, Hungary [email protected] http://irh.inf.unideb.hu/user/jsztrik
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Page 1: Queuing Theory General Introduction

Queueing Theory and its ApplicationsA Personal View

ICAI 2010, Eger, Hungary27 ‐ 30 January,  2010

János SztrikUniversity of Debrecen, Debrecen, Hungary

[email protected]

http://irh.inf.unideb.hu/user/jsztrik

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Outline

g Origin of Queueing Theory 

g Classifications of Queueing Systems

g Applications

g Solution Methods

g Basic Formulas and Laws

g Recent Developments

g Hungarian Contributions

g References

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Origin of Queueing Theory

Agner Krarup Erlang, 1878‐1929

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g "The Theory of Probabilities and Telephone Conversations", Nyt Tidsskriftfor Matematik B, vol 20, 1909. 

g "Solution of some Problems in the Theory of Probabilities of Significancein Automatic Telephone Exchanges", Elektrotkeknikeren, vol 13, 1917. 

g "The life and works of A.K. Erlang", E. Brockmeyer, H.L. Halstrom and ArnsJensen, Copenhagen: The Copenhagen Telephone Company, 1948.

Queueing Theory Homepage

http://web2.uwindsor.ca/math/hlynka/queue.html

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Murphy’s Law of Queue

g If you change queues, the one you have left will start to movefaster than the one you are in now.

g Your queue always goes the slowest

g Whatever queue you join, no matter how short it looks, willalways take the longest for you to get served.

Google search for “Queueing Theory “: 188 000

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Applications

g Telephony

g Manufacturing

g Inventories

g Dams

g Supermarkets

g Computer and Communication Systems

g Call Centers

g Infocommunication Networks

g Hospitals

g Many others

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Kendall’s Notation

David G. Kendall, 1918‐2007

A/B/c/K/m/Z7

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Performance Metrics

g Utilizations

g Mean Number of Customers in the System / Queue

g Mean Response / Waiting Time

g Mean Busy Period Length of the Server

g Distribution of Response / Waiting Time

g Distribution of the Busy Period

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Solution Methodologies

g Analytical

g Numerical

g Asymptotic

g Simulation

g Tools

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Erlang Loss Formulas, M/G/c/c Systems

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Pollaczek‐Khintchine Formulas, M/G/1 Systems

Felix Pollachek, 1892‐1981             Alexander Y. Khintchine, 1894‐1959 

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Mean Value Formulas

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Transform Formulas

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Little’s  Law

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Recent Developments

Boris Vladimirovich Gnedenko, 1912‐1995

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Recent Developments

Leonard Kleinrock, 1934 ‐

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Hot Topics

22nd International Teletraffic Congress

September 7‐9, 2010, Amsterdam, The Netherlands

• Performance of wireless/wired networks• Business models for QoS• Performance and reliability tradeoffs• Performance models for voice, video, data and P2P applications• Scheduling algorithms• Simulation methods and tools

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Madrid Conference, 2010

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Hungarian Contributions

Lajos Takács, 1924 ‐

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Hungarian Contributions

g Eötvös Loránd University ( A. Benczúr, L. Lakatos, L. Szeidl )

g Budapest University of Technology and Economics( L. Györfi, M. Telek, S. Molnár )

g University of Debrecen ( J. Tomkó, M. Arató, B. Almási, A. Kuki, J. Sztrik )

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MyMost Cited Paper, 22 citations

On the finite‐source QueueEuropean Journal of Operational Research 20  (1985) 261‐268 

Steady‐State Probabilities

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Performance Metrics

Utilizations

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Mean Values

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Java Applets and Information

g http://irh.inf.unideb.hu/user/jsztrik/education/09/index.html

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http://irh.inf.unideb.hu/user/jsztrik/

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Bibliography

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