Send More Riders

Predictive scheduling for an on demand delivery fleet

Transcript

1. Send More Riders!
   Predictive scheduling for an on demand delivery fleet
   @PeterOwlett
   

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2. High quality food, delivered fast and
   on demand
   

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4. Life of an order
   

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5. Life of an order
   

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6. Utilisation
   %
   Hour of Day (Colour = Day of Week)
   

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7. We need enough drivers to deliver on
   time, but not so many we lose money
   

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8. •Restaurants take longer than expected to make food
   •Items get missed - we have to go back and get them
   •Drivers become unavailable (flat tyre etc)
   •Customers hard to find
   It gets harder …
   

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9. Exam question
   How many drivers should we schedule for the
   next two weeks in each part of London over 15
   minute blocks?
   

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10. Before we dive
    in - a quick
    apology
    

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11. Lets formulate!
    Where
    • O is orders
    • d is date
    • z is zone
    

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12. Forecasting Daily Volume
    

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13. This book is awesome
    And Free!!! - https://www.otexts.org/fpp
    

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14. Forecasting Daily Volume
    

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16. Statsmodels supports this out of the box
    Forecasting Daily Volume
    # Decompose the raw time series
    decomposition = sm.tsa.seasonal_decompose(data.values, freq=7)
    # Extract individual components
    all_trend = decomposition.trend
    all_seasonal = decomposition.seasonal
    all_resid = decomposition.resid
    

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17. First Results
    

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18. Holidays (and Weather)
    Forecasting Daily Volume
    

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19. Improving the seasonal
    50%
    Improvement!
    

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20. •Vary the training range
    •Train on np.log(series) and transform back
    Signal in the noise?
    Looks
    Seasonal
    Looks
    Seasonal
    Random
    Noise
    

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21. Because we can chart each series,
    we can reason about how to improve
    our model
    

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22. Forecast each component
    Forecasting Daily Volume
    # Forecast Trend
    lm_lin = LinearRegression().fit(dates, trend_vals)
    forecast_trend = lm_lin.predict(forecast_window)
    # Forecast Seasonal
    seasonal_pattern = np.tile(base_seasonal_pattern,
    math.ceil(days_to_forecast / 7.0))
    forecast_seasonal = seasonal_pattern[0: days_to_forecast]
    

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23. Forecasting Daily Volume
    

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24. Where
    • O is orders
    • D is demand
    • E is efficiency
    • z is zone
    • d is date
    • w is weekday
    • t is time of day
    Converting Daily Orders to Driver Hours
    

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25. Zero to One Scale - neat trick
    Estimating Demand Curves
    scaled_series = df_mean_curves.order_volume /
    df_mean_curves.groupby(['zone', ‘day_of_week’])\
    .transform(np.sum).order_volume
    

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26. Estimating Demand Curves
    Ratio of daily
    orders per
    unit time
    Hour of Day
    

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27. Efficiency
    Orders per
    driver
    per hour
    Hour of Day
    

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28. Final Forecast
    

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29. Getting the forecast out into the real world
    

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30. Volumes to Shifts
    

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31. Deployment
    

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32. SUCCESS!!!
    

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33. 1. While not as powerful as R, Statsmodels does give
    you core time series tools
    2. Seasonal decomposition is very meaningful to
    human beings
    3. By using all python, we were able to ship quickly
    Stuff we learned
    

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34. Thanks!
    

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