Technical Details

This is what the SafeSeating Solutions app does for you.  Hosted in an Amazon Web Services (AWS) Elastic Cloud Computing (EC2) instance,  you get a link like{yourschool}, which goes to your own customized version application of the application. 

Once you log in, all you need to do is upload the venue manifest of available seats (from Paciolan, Ticketmaster, or any ticketing solution) and a listing of the groups that you want to safely seat, to include the quantity of tickets requested and other information. 

Example Data Files

Here are small examples of the information required in Excel (.xlsx) format. A comma-separated variable (.csv) formatted file is also fine.

In the example venue manifest file, you will see that there are a total of 40 seats represented, in 2 sections.  Section 1 is a more “regular” section, in that it has 2 rows of 8 seats each, with the rows numbered 1 and 2.  Note that Section 2 is more “irregular,” in that it has rows with both letters and numbers (A, 1, and 2).  Also, the number of seats in each row varies (6, 8, and 10, respectively).  The ‘RowOrder’ is necessary so that the model knows what order the rows are physical in.  Some venues have letters in front of numbers, while in other locations it is reversed.

In the example ticket list file, you will find 30 groups, with a total of 66 seats requested. For each customer, there are several data field required – a customer ID, a price level code and/or price level (description), the quantity of tickets for that group, and their priority (with higher being more important). Clearly, they will not all fit in the 40 seats available in the example venue manifest file.  And with restrictions due to COVID-19, the capacity really isn’t 40.  Therefore the information about the priority of each group, to ensure that those with the highest priority (based on type of relationship, years of season ticket holder status, or whatever you want) get seats before those with a lower priority.  The price level code and description files are for larger venues where you may want to run the model in an iterative manner, filling prime sections with customers who normally purchase good seats, before those whose tickets are in less desirable sections.

Providing venue managers with optimal seating arrangements, given social distancing and capacity constraints.