Future Dispatching
16
Future Dispatching Bill Cumpston and Jason Lawrie
description
Future Dispatching. Bill Cumpston and Jason Lawrie. REQUIREMENTS . TAXIS As many bookings as possible Wait times not major consideration Driver not normally relevant. HIRE CARS, WATS Control numbers of bookings Wait times critical for hire cars, important for WATs. Driver often relevant. - PowerPoint PPT Presentation
Transcript of Future Dispatching
Future DispatchingBill Cumpston and Jason Lawrie
REQUIREMENTS
TAXISAs many bookings as possibleWait times not major considerationDriver not normally relevant
HIRE CARS, WATS
Control numbers of bookingsWait times critical for hire cars, important for WATs.Driver often relevant
OPTIMISE Minimize Distance
OPTIMISE Current Rules
OPTIMISE Time
Various algorithms – zone, distance, coverFleets have different requirementsExplanations are complicatedNot yet perfect
Dispatch is Complicated
Pickup AddressesDestination AddressesRequested Pickup TimeASAP or Pre-BookingRequested Vehicle Attributes (WAT, etc.)
Our Current Algorithm Inputs
Preferred vehicle or driverCurrent passenger wait timeRelative priority of attributes (must do maxi)Driver plottingVehicle vacant timeBlacklist preferences
Our Current Algorithm Inputs
Driving time from current positionTime criticality (e.g. meeting a train)Current driver earnings per hourDriver rewards earnedDriver penalties incurred
It will not be getting simpler!
Driver end of shift time and locationDistribution (E.g. “trip” run fairness)Pre-allocation (E.g. private jobs)Distribution to sub-networks or “friends”Changes for peak or normal periods
And that’s not all ….
Customer experience vs. Cost Reduction?Driver fairness vs. rewards and penalties?
Many things are Trade-offs
High level categories provide a guideThis is combined with an “input” weightingAdd reward or penalty scoresCar or job with highest score wins
Weighting Based Algorithm
Can we eliminate zones?Perhaps have some “special” regions (ranks)All distance calculations based on actual directions (including current time of day)
Who Likes Zones and Layering?
“Their Score”Will increase over time until they get a jobRewards or penalties will affect their scoresIn a localised region (e.g. rank) the highest score in a similar vehicle will get job first
What will drivers see?
Click “Explain” on any offerGet scores for every vehicle for that offerCan explain to drivers if neededCan be used to tweak weighting
But why did .….. get job …… ?
THANK YOU!
Questions?
