Pull more summary data in DataCollector

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question

Jack L asked Nov 25, '19 Braydn T commented Dec 5, '19

Pull more summary data in DataCollector

preop-or-pacu-model-student-3-1.fsm excel-2019-11-23-17-55-42.png

Attached is a model My objective was to get summarized data you would find in the dashboard widgets (e.g. staff utilization) but have it populate for every experimented scenario. This is to expedite the experimentation analysis of different cases. Is this something you can do in the DataCollector? If so, what resources can I look through to code it?

Attached is the model and the default output for DataCollector

Software Version:

FlexSim HC 5.3.10

healthcare data collector

excel-2019-11-23-17-55-42.png (9.7 KiB)

preop-or-pacu-model-student-3-1.fsm (1.0 MiB)

Answer 1 · 2019-11-26T18:32:25Z

Braydn T answered Nov 26, '19 Braydn T commented Dec 5, '19

@Jack L

We have reviewed your request and have decided that it would not be fair to alter the model this late in the competition.

We think this is a good idea and we will implement to the competition in the future, but we intentionally added the experiment button in the dashboard to avoid confusion for participants in learning how to use the experimenter.

If you still would like to learn how to do this, I can send you instructions. If I do, we can’t allow you to do this to the model for the competition.

Thanks for participating!

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Jack L commented · Nov 27, 2019 at 05:12 PM

@Braydn T

Thanks for the reply! I understand you and your team's decision to not alter the initial model this late. If I manage to figure out how to modify the DataCollector into populating additional statistics per scenario on my own, would that be legal for me to use in the competition?

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Cliff King ♦ Jack L commented · Dec 02, 2019 at 08:05 PM

I don't see why not. Do you have any objections @Markus Cueva?

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Braydn T Jack L commented · Dec 05, 2019 at 06:30 PM

I think that would be fine to do in order to find your ideal numbers, but then you could run those numbers in an unmodified model to make sure you didn't inadvertenly change some of the models logic.

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