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View Code? Open in Web Editor NEWEvolutionary Scheduling Made Easy: an attempt to solve a common combined clustering and rostering problem with evolutionary algorithms.
License: GNU General Public License v3.0
Evolutionary Scheduling Made Easy: an attempt to solve a common combined clustering and rostering problem with evolutionary algorithms.
License: GNU General Public License v3.0
For optimal convergence we need to add support for different solving phases. For example, an iterator with different phases might look like this:
Currently the clustering solution is simply printed in the terminal. We need to add an option to specify a problem name (e.g. myteams
) and use that name to export two files: myteams_teams.csv
and myteams_schedule.csv
.
In the upcoming test, the number of timeslots differs per day. We could solve this by setting availability to 0 for all participants, but it would be better to enhance the program such that different timeslots per day are supported.
Currently matplotlib is blocking this.
The original proposal featured both clustering and scheduling. Earlier trials with clustering did not bear fruit.
A solution using evolutionary algorithm would be similar to the scheduling problem. Calculate the maximum group size m and the number of groups n; create a list with each group number repeated m times; the permutation determines the group assignment. Make two distinct assignments for women and men separately.
Normalize the different parameters (e.g. length, motivation, skill) and determine their weights. Find a suitable function for determining fitness. Add extra checks to enforce minimum group sizes.
Examples may be outdated.
The deap module supports multi-processor calculations. We should support that too.
For the demo we want to be able to call esme using Python Celery, and then report on progress using a custom progress bar. For this, we need to store intermediate results, either in file or in the database.
Create a class or function pattern for reporting intermediate progress.
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