Early, on Time, or Late Arrivals

To simulate people arriving for an event such as a doctor’s appointment, or to watch a movie, where some people arrive early, some on time, and some late.

Where to Find the Model Object

The following model object will be used to arrivals in ProcessModel:

• Arrivals \ Early, on time, or late.

How to Use the Model Object

1. Open your model: Open your model or create a new blank model.
2. Insert the Model Object into your model: Locate the appropriate location in your model where you would like to place the model object, move the view to the location and then insert the model object.
3. Connect to your model
   1. Connect the arrival route to an entity in your model.
   2. Create a route leaving the Arrival Generator and connect it to the first process in your model.
4. Change arrivals: Change arrival times and quantity to match your requirements.
5. Update Action Logic: Change the distribution in the action logic of Arrival Generator to match your distribution pattern. You only need to change the distribution in line 1, line 2 and 2 should not be changed.
6. Completion: The Early, on time, or late arrival model is now integrated into your model, you should now be able to save and then simulate the model.

Notes

As an example, this model object uses a single scheduled arrival 20 minutes prior to the target arrival time of 8:00am. The arrival occurs 20 minutes early (7:40). Then the storage uses a Triangular distribution to randomly releases the entities between 0 and 40 minutes after their initial arrival, with the mean value being 20 minutes after they enter the storage (or 8:00am).

To add additional arrivals simply add more scheduled arrivals at the desire “early” time.

Arrival Pattern

A Scheduled arrival is used here, but you may change to another arrival type as needed. In any case, you must enter your specific arrival times.

Action Logic

In Arrival Generator a random time value is assigned to each entity. After that waiting time, the starting cycle time and value added time attributes are set to 0 so that when the entity leaves the storage location, it will be as though the entity just arrived in the model.

Advance Topics

How This Model Object Works

Example: At the UVRMC outpatient clinic, people are told to arrive 15 minutes early to fill out paperwork. Over time they have collected data that shows that some people might arrive as early as 30 minutes prior to their appointment. Most people will arrive about 15 minutes early. Some people will be as much as 5 minutes late.

We have selected a triangular distribution to represent the pattern of arrivals. If the appointment was set for 8:00 AM, then the actual entry in the scheduled arrival would be 7:30. The diagram below shows the relationship of the distribution with the arrival time to create the early late pattern:

Although the actual appointment time is 8:00 AM, the entry in the scheduled arrival module is set to 7:30. The distribution in the Arrival Generator object is changed to read T(0,15,35). What is happening in the simulation is the arrival of an entity (patient) at 7:30. When the entity reaches the storage, a time between 0 and 35 minutes (with 15 being the most likely) is assigned to the entity. When the waiting time is finished, entity attributes are reset. So from a reporting standpoint, the entity appears to have just arrived as it leaves the storage.