A talk on how we use simulation-based optimization at Instacart to manage several tradeoffs and achieve supply/demand equilibrium when trying to solve how many shoppers we need to staff for.

1. Supply Optimization At Instacart
Jagannath Putrevu
SF Bay Area Machine Learning Meetup
5/3/2017

2. v
Supply Forecasting - McDonald’s
Single Server Queue
Service Time: 5 min/order
Orders per hour: 12
24 orders per hour
60 orders per hour
.
.
2 servers per hour
5 servers per hour
.
.
Demand Supply

3. v
Supply Forecasting - Chipotle?
Multi Server Queue
Variable Service Speeds Per Server
Demand Supply
24 orders per hour
60 order per hour
.
.
?
?
.
.

4. v
Supply Forecasting - Instacart?
What the talk will cover:
● Logistics @ Instacart
● Fulfillment Models
● Problem Formulation
● Simulation Based Optimization Framework
What it will not cover:
● Technology stack
● Algorithm details
● Results & Comparisons

5. v
Delivery Logistics
Customers Place
Orders
Orders Get
Shopped For
Orders Get
Delivered

6. v
Fulfillment Models
Due
#2 Ordered
Due#1 Ordered
Due#3 Ordered
Handoff
Model
Shopping Times Delivery Times
#1
#2 #3
#1 #3 #2
In-Store Shopper 1
In-Store Shopper 2
Driver 1
Handoff
Pickup
Bags

7. v
Handoff Model - Staging Area

8. v
Fulfillment Models
Full Service
Model
Shopping Times Delivery Times
Due
#2 Ordered
Due#1 Ordered
Due#3 Ordered
Full Service Shopper 1 #3 #2#1#1 #2 #3

9. Store With Personal Shoppers
Full Service Shopper
Customer

10. Store With Personal Shoppers
Full Service Shopper
Customer

11. v
What is Efficiency?
Efficiency =
Deliveries
Total Time
Deliveries
Active Time
Active Time
Total Time
= X
Active Efficiency= X Utilization
How fast? How busy?

12. v
Time Components In Labor Hours
Begin Shift End Shift
Deliver to Customer
Wait for Batch
Drive to Store Shop Groceries
Pickup Bags from Store

13. v
Time Components In Labor Hours
Deliver to Customer
Wait for Batch
Drive to Store Shop Groceries
Pickup Bags from Store
End ShiftBegin Shift
In-store Shopper

14. v
Time Components In Labor Hours
Deliver to Customer
Wait for Batch
Drive to Store Shop Groceries
Pickup Bags from Store
Full Service
Begin Shift
FullServiceShopper
FullService
FullService
End Shift

15. v
Time Components In Labor Hours
Deliver to Customer
Wait for Batch
Drive to Store Shop Groceries
Pickup Bags from Store
Driver
DriverDriverBegin Shift
Driver
End Shift

16. v
Time Components And Factors Affecting Them
Idle Time: Supply/Demand equilibrium, Variance in Cancellations
How busy?
How fast?
Driving Time: Batching (Number of Orders Per Trip), Proximity To Store, Traffic
Picking Time: Speed of Shopper, Shopping List Sorting, Checkout Times
Bags Pickup Time: Staging Area Layout, Number of Bags being picked up
Delivery Time: Traffic, Order Time/Space Density, Routing Algorithm Efficiency

17. v
Supply/Demand Equilibrium
Idle Time
Lost Deliveries

18. v
Problem Statement
● For a given day, how do we staff :
○ In-store shoppers (ISS) at each store location,
○ Full Service shoppers (FSS) across the entire zone,
● so as to:
○ maximize active efficiency,
○ minimize idleness,
○ minimize lost deliveries

19. v
Final goal?
8 AM 9 AM 10 AM 11 AM 12 PM 1 PM 2 PM 3 PM 4 PM 5 PM 6 PM 7 PM 8 PM
Whole Foods
Soma
2 3 4 5 6 6 6 5 5 5 4 3 2
Whole Foods
Castro
? ? ? ? ? ? ? ? ? ? ? ? ?
Costco ? ? ? ? ? ? ? ? ? ? ? ? ?
Safeway ? ? ? ? ? ? ? ? ? ? ? ? ?
Full Service 20 30 40 50 60 60 60 50 50 50 40 30 20

20. v
Decision Factors
Handoff Model Full Service Model
Speed
Location
Overhead
Task Handling

21. v
Everything Has Variance
● Demand
● Space-Time Density
● Traffic
● Weather
● Shopper Abilities
● Cancellations

22. v
Cost Functions
Desired Actual
Idleness Cost
Lost Deliveries Cost
Lost Efficiency Cost

23. v
System
Outputs
Number of Shoppers Required
Percentage of Volume Done Through
Handoff
Estimated Efficiency & Utilization
Inputs
Demand
(variable)
Store Locations
(fixed)
Shopper Pool
(variable)
Fulfillment Times
(predicted, variable)
Cancellation Probability
(predicted)
Handoff vs Full-Service
Idleness vs Efficiency vs Lost
Deliveries
Store Timings Shift Length Requirements Business rules
Constraints

24. v
Simulate An Ideal Universe
Assume infinite Supply, solve the fulfillment problem, and generate optimal set of trips

25. v
Staff For An Ideal Universe
● When do all the trips begin and end in the optimal solution?
● Back-calculate how many shoppers we need and at what times we need them

26. v
Results From One Universe

27. v
Aggregate Results From All Simulations

28. v
Final ‘Optimal’ Set of Staffing Levels

29. v
Losses In One Universe
1
1
1
2
2
2
1
2
Idleness Cost
Lost Efficiency Cost
(and/or)
Lost Deliveries Cost
x_8
x_9
x_10
x_11
x_12
x_13
x_14
x_15
x_16
x_17
x_18
x_19
x_20

30. v
Minimize Losses Across All Universes
Min. ∑ (Idleness Cost + Lost Efficiency Cost + Lost Deliveries Cost)

31. v
Final Output
8 AM 9 AM 10 AM 11 AM 12 PM 1 PM 2 PM 3 PM 4 PM 5 PM 6 PM 7 PM 8 PM
Whole Foods
Soma
12 12 16 16 14 14 12 12 10 10 8 6 4
Whole Foods
Castro
5 5 5 5 4 4 4 3 3 3 3 2 0
Costco 0 0 10 10 10 8 8 8 6 6 6 0 0
Safeway 2 3 4 5 6 6 6 5 5 5 4 3 2
Full Service 20 60 100 120 150 180 180 180 150 150 80 50 30