This document discusses using AI to optimize bus depot operations for Transports publics fribourgois, which plans up to 2000 daily trips with over 220 vehicles across 9 depots. The proposed solution uses a two-step iterative algorithm combining a greedy algorithm and local search optimizations to solve the traveling salesman problem of efficiently scheduling trips in real-time. An AI-based architecture and demonstration are presented.

1. LevKiwi.ch@Trivadis
AI & IoT in a real life scenario
How to operate a complex bus depot with AI?
Lev Kiwi & Ulises Fasoli

2. Lev Kiwi
Consultant in AI, Data Science &
Business Analytics
Lev.Kiwi@trivadis.com
+41 58 459 53 75
Ulises Fasoli
Senior Consultant Big Data &
Data Science
Ulises.Fasoli@trivadis.com
+41 58 459 58 04

3. Use Case
Transports publics fribourgois

4. Speaking in numbers...
Up to 2000 trips to plan
with more than 220 vehicles (with characteristics)
over a network of 9 depots (with constraints)
...in real time !

5. Solution

6. ADepot B C
07:30 07:40
09:50
10:10
The mathematical model

7. The Artificial Intelligence
Two-step iterative algorithm
1. Greedy algorithm
2. Local Search optimizations

8. Illustration of the algorithm
Travelling salesman
problem

9. Architecture

11. Demo time!