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
2. High quality food, delivered fast and on demand
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
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!