This is the presentation I gave for my Industrial Placement as part of my Master's Degree in Computing at Imperial College London.

1. Lorenzo Paoliani Industrial Placement 2016
MEng Computing | Imperial College London
Fleet Management and Optimisation
code that delivers

2. Pie is a solution for logistics and
transportation companies to manage their
vehicles and power their operations.
PLANNING TRACKING ROUTING
Manage drivers and fleet
Register customer orders
Track vehicles on the road
App for drivers
Route vehicles from A to B
Route around restrictions
for large vehicles, road
closures, etc.

3. Apple Pie

4. Over the course of my
placement I focused on 3 areas
UI UX DX
User
Interface
User
Experience
Developer
Experience

5. Storybook
DX

6. Storybook
• Separates “pure” view components from
the main app
• Allows to describe the intent behind a
component by describing a story of its
possible rendering states
• Distraction free environment
• Quickly iterate
• Communicate with the design team
• Track use cases, error and loading states

7. FiltersUI UX

8. Filters
• Logic and UI to filter deeply nested data
• Filters pile on top of each other
• Recursively descends into an entity
checking whether the current piece of
information is hidden or visible
• At every level, after visiting the children
nodes, the parent decides its status
• This allows to mark every node as one of
VISIBLE / DISABLED / HIDDEN

10. Cuttlefisha network wide route optimisation engine
UX DX

11. The Problem

12. The Problem
• 100+ locations to dispatch vehicles
• Thousands of vehicles
• Pick up freight from 300+ locations all over the UK every day
• Sort the deliveries and send them towards the right regional
depot
• Must arrange a plan to fulfil all the orders

13. The Problem
• 130+ locations to dispatch vehicles
• Thousands of vehicles
• Pick up freight from 300+ locations all over the UK every day
• Sort the deliveries and send them towards the right regional
depot
• Must arrange a plan to fulfil all the orders
Right now, this is done in an
office, by hand, every day.

14. Engine

15. based on a 2011 paper:
An Iterated Local Search heuristic for the
Heterogeneous Fleet Vehicle Routing Problem
• Defines the HFVRP and its subcategories
• 2 hard problems in computer science
• Travelling Salesman Problem
• Bin Packing Problem
• Searches solutions iteratively through a small subset
of similar solutions from the solution space
• Uses random perturbation of candidate solutions to
escape local minima
• Any solution - even no optimisation! - is better than
the current state of the long haul logistics
Penna, P.H.V., Subramanian, A. & Ochi,
L.S. J Heuristics (2013) 19: 201. doi:
10.1007/s10732-011-9186-y

16. API + Solver
• Exposes an API to build and solve a
Heterogeneous Fleet Vehicle Routing
Problem
• Input: a set of dispatching locations and a
set of pickup jobs
• Output: a fulfilment plan that connects
jobs and dispatchers
• Handles pickup time, service time,
volume, and weight constraints
• Selects best vehicle type to service a route

17. App

18. Build a Problem

19. Plot the Solution

21. Technology
graphhopper/jsprit
an open source implementation
of the algorithms described in
the HFVRP paper
Statically typed
programming language
for the JVM
Engine App

22. Lorenzo Paoliani
lorenzo.paoliani@gmail.com
Industrial Placement 2016
MEng Computing | Imperial College London
Thanks!