TABLE OF CONTENTS
- Preamble
- Understanding Auto-Rooming Results
- Improving Auto-Room Results
- Auto-Rooming Vs Manual Rooming
- Further Reading
Preamble
E10's auto-room function calculates the best rooming solution based on the requirements provided. This article will assist users in diagnosing the results of auto-rooming so that rooming requirements can be incrementally adjusted, resulting in a solution that best suits school needs.

Detailed information on how to use the Auto-room function can be found here:

A webinar recording exploring best rooming practice can be found here:
Understanding Auto-Rooming Results
Score
Once an auto-room is performed, a table will appear that lists the rooming results for each class. Classes will be ordered according to their 'score'; the total penalty of the rooming requirements not satisfied. In other words, the rows at the top are the classes that the algorithm couldn't perfectly room according to existing rooming requirements.
Double click on a class's score cell to get more information about how the score was calculated:
Allocated Rooms
Allocated rooms are given the following colour coding:
Green | Preferred rooms | |
Orange | Fallback rooms | |
Red | Desperate rooms | |
Maroon | Generalist room used as fallback |
Investigating Alternative Rooms
To get more information about why the class has not been placed in a preferred or alternate room, double click on the allocated room.
This will produce a table showing the currently allocated rooms (striped) for that class as well as other classes' rooming for those periods. In the example below, we can see that 11JAP1 was not placed in its preferred room of R14 for three periods due to 12GRM1.
Rooming Quality
The auto-rooming algorithm seeks to minimise the total room quality score. As the score is created by the weighted penalties of room preference violations, the lower the score the better. The calculation of this score can be viewed by pressing on the Rooming Quality icon in the action bar to the right of the screen. The Raw column gives the number of instances of preference violations, while the Weighted column shows the actual penalty score given due to the weighting preferences.
By investigating this screen, we can see exactly which preferences have been considered, and how much of an impact they had on the final result.
Improving Auto-Room Results
Auto-rooming is best thought of as an iterative process. Once initial auto-room results have been reviewed, rooming parameters can be adjusted in order to obtain an auto-room result that best reflects school needs. Often, a particular parameter or preference that has been overlooked can significantly improve the end result.
Below is a list of rooming parameters that may be worthwhile considering.
- Room data (F5). Check room homeroom or home group entries. Ensure size entry is accurate. Discriminate room types between Generalist preferred G, fallback (G), desperate ((G)), and Specialist S
- Check Subject room data (Shift-F5) . Ensure enough rooms have been been assigned in the set. Consider designating more fallback rooms for classes that have been assigned multiple rooms.
- Check Settings > Rooms for 'over-crowd' and 'specialist rooms to be shared' evenly classifications.
- Check Class data (F6) at each year group for any hard coded or further restricted rooming. Generally these fields should be left blank leaving the greyed-out room sets to remain. Entries here to over-ride these room sets for individual classes should be used discerningly.
- Check the weights used in Rooming Parameters when auto-rooming. The default settings are found to yield best results unless schools have particular needs.

Further information can be found in the Knowledge Base article below.

Further information can be found in the Knowledge Base article below.
Tips and tricks
- Look closely at the colours of each room assignment. If a room assignment is undesirable for a certain class but displayed in green, rooms sets should be adjusted. Conversely, if a class has been assigned a room that is acceptable but is coloured orange or red, consider adding these rooms as preferred in subject room sets for that course.
- Investigate classes with a high score by double clicking on the score for that class. If penalties are being given for criteria that are acceptable consider experimenting with auto-room weights.
- Investigate classes that have been placed in a large number of distinct rooms. This will normally occur when a large number of classes are demanding a small number of rooms. Try adding to the subject room sets and auto-rooming again to see the results.
- If you are unsure as to why E10 is placing a class in a certain room, first view the total room quality score and then try making a change manually. To do this, click on the allocated room, and then select the desired room, as below.
Now view the total room quality score again. If the score has gone up this means that overall, this is an inferior result according to the current room preferences. Look closer at the rooming quality table to understand why.
Auto-Rooming Vs Manual Rooming
Although it can be tempting to micro-manage rooming assignments - either through hard-coding in the class data screen or by manually assigning all rooms - it is worth persisting in incrementally adjusting subject room sets and weightings until auto-rooming gives the desired results. You will develop a better understanding of the rooming pinch points of your timetable and ultimately develop a solution that is of a higher quality that can be easily repeated upon each construction.
If you are still not getting the results you are after through auto-rooming, consider reading one of the Knowledge Base articles below.
Further support is available through lodging a ticket with a timetable specialist by pressing the 'Get Help' button in the bottom right hand side of any screen in E10 and detailing what you are trying to achieve.
Further Reading
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