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The auto-scheduling algorithm, in addition to the above heuristics, tries to minimize the average euclidean distance between each activity. This was also done to generate better schedules: given a set of activities, the user wants to complete as many as possible in a fixed amount of time. The algorithm internals are traveling-salesman-like: we have a set of unscheduled items which we add to the schedule in different ways (while observing the "principle of least destruction"). Then, we choose the best complete configuration based on the weight function we described above plus the distance metric. Despite our small problem scale, the brute force approach did not work time-wise; so our algorithm is greedy, with intelligence to avoid getting stuck in bad schedules.
Evaluation
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Reflection
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