1. Real Time Fulfilment Planning at
Flipkart Scale
Jagadeesh Huliyar
jagadeesh.huliyar@gmail.com
The Fifth Elephant 2016
29th July 2016
2. What is it?
➔ Flipkart stores and sells
millions of unique items
through its Fulfillment
Centers (FCs) and Sellers.
➔ These items need to be
picked from FCs or Seller
Locations and delivered to
End Customers.
➔ Real Time Fulfilment
Planner is responsible for
planning the schedule for
these activities.
4. Decisions
Are there any decisions to be made?
When should pick start in the FC? What is the order or batch for pick?
When should other activities be performed? Should items be held somewhere?
Which transport connection and mode should be taken?
Which route should be taken during transportation?
Do we really need to take these decisions? What happens if we process as and
when order are placed or as and when shipments arrive at a particular location?
7. Storage Usage
➔ Hour 1
◆ Operation-1 processes 75 small units.
◆ Operation-2 will be able to process 50 small units and remaining 25 will remain in storage.
➔ Hour 2
◆ Operation-1 processes 50 large units.
◆ Operation-2 can processes these 50 large units plus 25 small present in storage.
➔ This average processing over two hours is 62 units and at the end of two hours storage is empty.
9. Priority, Breach Cost and Capacity Consumption
Case 1 Case 2
Supply 2 units 2 units
Demand 2 COD and 2 Prepaid. 2 COD and 2 Prepaid.
Service Type All Regular COD are NDD.
Pre-paid are Regular
Consumption COD = 2 units.
Prepaid = 1 unit
COD = 2 units.
Prepaid = 1 unit
Breach Cost Regular = 20 rupees
NDD = 70 rupees
Regular = 20 rupees
NDD = 70 rupees
Decision 2 Prepaid 1 COD
10. Priority, Breach Cost and Capacity Consumption
➔ Consumption Factory vary as per Stage of
processing
◆ Sortation Station
● Size
◆ FC
● Proximity to packing stations
● Packaging Type
◆ Delivery Centre
● Payment Type
● Address Type
➔ Breach cost or Profit on timely delivery
◆ Service Type
◆ Customer Type
11. Gigantic Graph
➔ Items : Different dimensions, Volume and Weight → Different
restrictions and shipping costs.
➔ Constraints
◆ Transport Connection Time
◆ Priority of Shipments
◆ SLA
Need to do all this at Lowest Possible Cost
12. Variability
➔ Demand Variability
◆ Destination Mix Variability
◆ Product Mix Variability
◆ Seller Mix Variability
◆ Service Mix Variability
➔ Supply Variability
◆ Dependency on temporary work
force
◆ Unreliable Transport Capacity
◆ Network and infrastructure issues
◆ Changing rules and regulations
17. Plan Creation
➔ Computation Graph Creation
➔ Start and Terminal Nodes Identification
➔ Feasible Set
➔ Optimisation Function
➔ Allocation
➔ Learning
➔ Usage of Learning
➔ Repeat
➔ Terminating Conditions
➔ Match
18. Scalability
➔ Nodes - Millions
➔ Edges - Tens of millions
➔ Need to distribute the
computation
19. Bulk synchronous parallel and Pregel
➔ BSP system consists of
◆ Local Memory Transactions.
◆ Message routing across Components.
◆ Synchronisation
➔ Pregel - Inspired by BSP
◆ Graph is divided into Partitions
◆ Algorithm is modelled as Computation
at Vertex and Message Passing across
Vertices.
◆ Sequence of supersteps
23. Real Time Fulfillment Plan
➔ Vertices - Time Slots in a Facility or Transport
➔ Edges - Movement of Shipments from one Time Slot in a Facility/Transport to
another Time Slot in a Facility/Transport
➔ Input - Requests Received and Forecast grouped by Dimensions
➔ Messages - Allocation groups and Learnings (Bottlenecked Dimensions and
Variable Capacity Requirement)
➔ Output - Set of Plans : Dimension Group : Time Slot : Number of Shipments
24. Simulated Annealing - Global Optima and Local Optima
➔ Starting with an initial solution. Learn better
allocation strategies. Move to a better
neighbouring solution.
➔ It can lead to situations where you're stuck at
a sub-optimal place.
➔ Simulated annealing injects right amount of
randomness into things to escape local
maxima.
25. Local Planning
Once Global Plan is ready there are some local decisions to be taken. These do
not affect the Global Plan and are hence taken independently
➔ Pick Path Optimisation
➔ Sorting Configuration
➔ Last Mile Vehicle Routing Problem
26. References
➔ Distributed multi-agent optimization
◆ http://www.cds.caltech.edu/~murray/preprints/ttm11-ifac_s.pdf
➔ Apache Giraph
◆ http://www.slideshare.net/ClaudioMartella/giraph-at-hadoop-summit-2014
◆ http://giraph.apache.org/
◆ http://www.apress.com/9781484212523
➔ BSP
◆ https://en.wikipedia.org/wiki/Bulk_synchronous_parallel
➔ Reactive Search and Intelligent Optimization
◆ REACTIVE SEARCH AND INTELLIGENT OPTIMIZATION Roberto Battiti, Mauro Brunato, and Franco Mascia
➔ Flow shop scheduling
◆ http://faculty.ksu.edu.sa/ialharkan/IE428/Chapter_4.pdf
◆ https://en.wikipedia.org/wiki/Flow_shop_scheduling
➔ Ant colony optimization
◆ http://www.scholarpedia.org/article/Ant_colony_optimization
◆ M. Dorigo, M. Birattari & T. Stützle, 2006 Ant Colony Optimization: Artificial Ants as a Computational Intelligence
Technique. TR/IRIDIA/2006-023
➔ Simulated Annealing
◆ https://en.wikipedia.org/wiki/Simulated_annealing
➔ Images have been picked by searching Google Images
