Send More Riders

Transcript

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

   fleet @PeterOwlett

2. High quality food, delivered fast and on demand

3. None

4. Life of an order

5. Life of an order

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

7. We need enough drivers to deliver on time, but not

   so many we lose money

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 …

9. Exam question How many drivers should we schedule for the

   next two weeks in each part of London over 15 minute blocks?

10. Before we dive in - a quick apology

11. Lets formulate! Where • O is orders • d is

    date • z is zone

12. Forecasting Daily Volume

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

14. Forecasting Daily Volume

15. None

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

17. First Results

18. Holidays (and Weather) Forecasting Daily Volume

19. Improving the seasonal 50% Improvement!

20. •Vary the training range •Train on np.log(series) and transform back

    Signal in the noise? Looks Seasonal Looks Seasonal Random Noise

21. Because we can chart each series, we can reason about

    how to improve our model

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]

23. Forecasting Daily Volume

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

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

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

    Hour of Day

27. Efficiency Orders per driver per hour Hour of Day

28. Final Forecast

29. Getting the forecast out into the real world

30. Volumes to Shifts

31. Deployment

32. SUCCESS!!!

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

34. Thanks!