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I have a list of a week's worth of tasks (about 2,000) that need to be done within a given timeframe ordered by how many minutes the task can be moved. Each task takes 1 hour. Table name is Task_List. We have 5 teams that can perform 1 taks at a time, so 5 different tasks can be ongoing.

Task Earliest_Start_Task Latest_Start_Task Window
1 2022-09-07 07:35:00 2022-09-07 07:35:00 0
2 2022-09-07 08:00:00 2022-09-07 08:00:00 0
3 2022-09-07 17:43:00 2022-09-07 17:49:00 6
4 2022-09-07 09:49:00 2022-09-07 10:00:00 11

I am trying to determine a schedule for the optimal start times for these tasks given the following constraints:

  1. The tasks must be started by the Latest_Start_Task time for each task
  2. Avoid going over the maximum of 5 at one time during the 1 hour duration of each task. It will be inevitable, but I want to minimize these occurrences as much as possible. Ideally, the times where the task count goes over the threshold of 5, I would like to identify which task is causing the overtime and when.
Phil
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JVP
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    Please see [https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example). This question is not reproducible - it is not really a coding question - so I am voting to close. You might want to look at packages like [criticalpath](https://cran.r-project.org/web/packages/criticalpath/vignettes/criticalpath-introduction.html). Once you have written some code to attempt to solve the problem, feel free to make the question a bit more focused, or ask another. – SamR Jul 11 '22 at 16:48
  • ^ agree this question isn't reproducible, and not clearly defined enough to answer as is. You might look at the `IpSolve` package and search for "linear optimization." – Jon Spring Jul 11 '22 at 17:06

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