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Arena Detailed Modeling. Nonstationary Arrival Process Stationary Poisson Process – mean rate is a constant Nonstationary Poisson Process – mean arrival.

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Presentation on theme: "Arena Detailed Modeling. Nonstationary Arrival Process Stationary Poisson Process – mean rate is a constant Nonstationary Poisson Process – mean arrival."— Presentation transcript:

1 Arena Detailed Modeling

2 Nonstationary Arrival Process Stationary Poisson Process – mean rate is a constant Nonstationary Poisson Process – mean arrival rate is a function of time –Simulation provide an approximate solution if the rate change between a predefined period was rather small

3 Nonstationary Arrival Process Example: Call Center –Call arrival rate - Customer calls for technical support, sales information, and order status TimeRateTimeRateTimeRateTimeRate 8:00~8:302010:30~11:00751:00~1:301103:30~4:0090 8:30~9:003511:00~11:30751:30~2:00954:00~4:3070 9:00~9:304511:30~12:00902:00~2:301054:30~5:0065 9:30~10:005012:00~12:30952:30~3:00905:00~5:3045 10:00~10:307012:30~1:001053:00~3:30855:30~6:0030

4 Balking Example –A call generated by nonstationary Poisson Process is really a customer trying to access one of the 26 trunk lines. If all 26 lines are currently in use, a busy signal is received and the customerdeparts the system. The term for this is balking.

5 Three-Way Decisions Example –Call types –Proper type of service

6 Sets Entity Machine 1 Machine 2 Machine 3 Set A pool of resources Similar but not quite identical Member of the set Can belong to another set

7 Sets Example – Technical Support Schedules NameProduct Lines12345678910111213141516171819202122 Charity1XXXXXXXXXXXXXXXX Noah1XXXXXXXXXXXXXXXX Molly1,3XXXXXXXXXXXXXXXX Anna1,2,3XXXXXXXXXXXXXXXX Sammy1,2,3XXXXXXXXXXXXXXXX Tierney2XXXXXXXXXXXXXXXX Sean2XXXXXXXXXXXXXXXX Emma2XXXXXXXXXXXXXXXX Shelley3XXXXXXXXXXXXXXXX Jenny3XXXXXXXXXXXXXXXX Christie3XXXXXXXXXXXXXXXX

8 Variables The variables module allows you to define your own global variables and their initial values

9 Submodels When developing large and complex models, it is often helpful to partition the model into small models, called submodels, that may or may not interact Example: –Create and direct arrivals –Technical support calls –Sales calls –Order-status calls

10 Terminating or Steady-State A terminating simulation is one in which the model dictates specific starting and stopping conditions as a natural reflection of how the target system actually operates. A steady-state simulation is one in which the quantities to be estimated are defined in the long run; i.e., over a theoretically infinite time frame.

11 Create Arriving Calls If a trunk line is available – seize Assign arrival time Delay for recording Determine call type Direct call and assign call type Else count balked call increment busy per period variable Dispose of call

12 Technical support calls Delay for recording Determine product type Seize technical support person Save product type and call start time Delay for call Release technical support person and trunk line Record call and line time Record tech line time

13 Technical support returned calls If return call required assign entity type delay for investigation determine product type seize trunk line and technical support person delay for return call release technical support person and trunk line record return call time dispose Else dispose

14 Sales calls Seize sales person Delay for call Release sales person and trunk line Record sales call time Dispose

15 Order Status calls Delay for call If customer wants to speak to a real person seize sales person delay for call release sales person Record call line time Release trunk line dispose


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