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_posts/2024-11-05-building-an-assignment-algorithm-3.markdown

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 The third and final blog of the series, well done for making it thus far! We will look at the last piece of the puzzle - slot sorting, which can make a substantial difference to the outcome of our algorithm. Then we will wrap up - looking at how all the elements of the algorithm discussed in the past 3 blogs in this series come together. You can find the [first blog here]({{site.baseurl}}/2024/10/24/building-an-assignment-algorithm-1.html), and the [second blog here]({{site.baseurl}}/2024/11/04/building-an-assignment-algorithm-2.html). 
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 The plot thickens if there are duplicate talks. Duplicate talks are the same talk given in different time slots - if for example, the talk is thought to be popular or important. Obviously, attendees shouldn’t attend the same talk twice, so care must be taken in not assigning the same talk twice. We won’t go into this in too much depth, but this does affect slot sorting. 
 
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 Firstly, slots containing the most duplicate talks go first. Duplicate talks are put at the front so that the most forced choices happen at the beginning, meaning that the attendees’ aggregate compromise has time (/remaining slots to assign) to even out, in comparison to other attendees, by the end of the algorithm.  
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 Next, we sort by how spread-out choices are. Duplicate talk slots with an even spread of choices to go first. This way, users aren't assigned a bad set of choices because the good assignments are no longer possible. This would be due to the previously assigned slots, which were oversubscribed (and therefore compromise high). Whereas as we discussed earlier, we want non-duplicate talk slots with a small spread (ie as many oversubscribed talks as possible) to go first. This is so that again, we generate as much compromise at the beginning of the algorithm run as possible, which will then even out over all the delegates by the end. After trialling and testing this method, we found it led to optimal results. 
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