Lorenzo Paoliani Industrial Placement 2016

MEng Computing | Imperial College London
Fleet Management and Optimisation
cod...
Pie is a solution for logistics and
transportation companies to manage their
vehicles and power their operations.
PLANNING...
Apple Pie
Over the course of my
placement I focused on 3 areas
UI UX DX
User
Interface
User
Experience
Developer
Experience
Storybook
DX
Storybook
• Separates “pure” view components from
the main app
• Allows to describe the intent behind a
component by descr...
FiltersUI UX
Filters
• Logic and UI to filter deeply nested data
• Filters pile on top of each other
• Recursively descends into an enti...
Cuttlefisha network wide route optimisation engine
UX DX
The Problem
The Problem
• 100+ locations to dispatch vehicles
• Thousands of vehicles
• Pick up freight from 300+ locations all over t...
The Problem
• 130+ locations to dispatch vehicles
• Thousands of vehicles
• Pick up freight from 300+ locations all over t...
Engine
based on a 2011 paper:
An Iterated Local Search heuristic for the
Heterogeneous Fleet Vehicle Routing Problem
• Defines the...
API + Solver
• Exposes an API to build and solve a
Heterogeneous Fleet Vehicle Routing
Problem
• Input: a set of dispatchi...
App
Build a Problem
Plot the Solution
Technology
graphhopper/jsprit
an open source implementation
of the algorithms described in
the HFVRP paper
Statically type...
Lorenzo Paoliani
lorenzo.paoliani@gmail.com
Industrial Placement 2016

MEng Computing | Imperial College London
Thanks!
Fleet Management and Optimisation - Industrial Placement Presentation
Fleet Management and Optimisation - Industrial Placement Presentation
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Fleet Management and Optimisation - Industrial Placement Presentation

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

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Fleet Management and Optimisation - Industrial Placement Presentation

  1. 1. Lorenzo Paoliani Industrial Placement 2016 MEng Computing | Imperial College London Fleet Management and Optimisation code that delivers
  2. 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. 3. Apple Pie
  4. 4. Over the course of my placement I focused on 3 areas UI UX DX User Interface User Experience Developer Experience
  5. 5. Storybook DX
  6. 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. 7. FiltersUI UX
  8. 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
  9. 9. Cuttlefisha network wide route optimisation engine UX DX
  10. 10. The Problem
  11. 11. 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
  12. 12. 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.
  13. 13. Engine
  14. 14. 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
  15. 15. 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
  16. 16. App
  17. 17. Build a Problem
  18. 18. Plot the Solution
  19. 19. Technology graphhopper/jsprit an open source implementation of the algorithms described in the HFVRP paper Statically typed programming language for the JVM Engine App
  20. 20. Lorenzo Paoliani lorenzo.paoliani@gmail.com Industrial Placement 2016 MEng Computing | Imperial College London Thanks!

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