Inconsistent Interarrival Times

Forums Academic discussion Simulation Inconsistent Interarrival Times

This topic contains 4 replies, has 4 voices, and was last updated by  Dolina Maharjan 6 months, 2 weeks ago.

Viewing 5 posts - 1 through 5 (of 5 total)
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  • #2449

    Andrew Michaletz
    Participant

    My team collected data at Prarie Fire in Union South from 8-9 AM on Tuesdays and Thursdays. We noticed that more people would arrive towards the end of our shift than in the beginning. When all of these are pooled together, it resembles a beta distribution, but this does not take into account the fact that short interarrival times are clustered at the back end of the time. When we run our model, lines do not build up like we saw because of the shift. Does anyone have any suggestions on how to handle this situation?

    #2460

    Kaibo Liu
    Keymaster

    Reply to Andrew Michaletz of #2449:

    Have you considered this as a non-homogeneous Poisson process? I guess one fixed distribution may not fit your data well.

     

    #2462

    Nicole Boehne
    Participant

    Another way to deal with the situation would be to simply split up the data into two different models. If your goal is simply to help Prairie Fire improve their process, then using two models may more accurately reflect how the system is working, and help Prairie Fire reallocate their workers and resources accordingly.

    #2485

    Andrew Michaletz
    Participant

    Reply to Nicole Boehne of #2462: Thanks Nicole, that was my gut instinct.

    #4066

    Dolina Maharjan
    Participant

    This is a really old post but I was curious if anyone could explain further on how would you split this data to create two models? Are you suggesting just having two models one for the first part of the shift and one for the end

     

    Reply to Nicole Boehne of #2462:

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