moduleD

moduleD - D-1 Operations Operations Management Management...

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Unformatted text preview: D-1 Operations Operations Management Management Waiting-Line Models Waiting-Line Models Module D Module D D-2 Outline Outline Characteristics of a Waiting-Line System. Arrival characteristics. Waiting-Line characteristics. Service facility characteristics. Waiting Line (Queuing) Models. M/M/1: One server. M/M/2: Two servers. M/M/S: S servers. Cost comparisons. D-3 First studied by A. K. Erlang in 1913. Analyzed telephone facilities. Body of knowledge called queuing theory. Queue is another name for waiting line. Decision problem: Balance cost of providing good service with cost of customers waiting. Waiting Lines Waiting Lines D-4 The average person spends 5 years waiting in line!! The other line always moves faster. 1995 Corel Corp. Thank you for holding. Hello...are you there? Youve Been There Before! Youve Been There Before! D-5 Bank Customers Teller Deposit etc. Doctors Patient Doctor Treatment office Traffic Cars Traffic Controlled intersection Signal passage Assembly line Parts Workers Assembly Situation Arrivals Servers Service Process Waiting Line Examples Waiting Line Examples D-6 Arrivals : Customers (people, machines, calls, etc.) that demand service. Service System : Includes waiting line and servers. Waiting Line (Queue) : Arrivals waiting for a free server. Servers : People or machines that provide service to the arrivals. Waiting Line Components Waiting Line Components D-7 Car Wash Example Car Wash Example D-8 Higher service level (more servers, faster servers) Higher costs to provide service. Lower cost for customers waiting in line (less waiting time). Key Tradeoff Key Tradeoff D-9 Queue : Waiting line. Arrival : 1 person, machine, part, etc. that arrives and demands service. Queue discipline : Rules for determining the order that arrivals receive service. Channels : Parallel servers. Phases : Sequential stages in service. Waiting Line Terminology Waiting Line Terminology D-10 Input source (population) size. Infinite : Number in service does not affect probability of a new arrival. A very large population can be treated as infinite. Finite : Number in service affects probability of a new arrival. Example: Population = 10 aircraft that may need repair. Arrival pattern. Random : Use Poisson probability distribution. Non-random : Appointments. Input Characteristics Input Characteristics D-11 Number of events that occur in an interval of time. Example: Number of customers that arrive each half-hour. Discrete distribution with mean = Example: Mean arrival rate = 5/hour ....
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moduleD - D-1 Operations Operations Management Management...

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