Optimised location, timings, and amount of vehicles to move, accounting for labour costs – this is because vehicles have to be moved between different charging points due to a difference in demand and varying popularity of pick-up/return points. Trips were simulated & discrete optimisation was applied
View the Python notebook here: https://github.com/karinekode/Optimisation-carsharing/blob/main/Python%20Model%20-%20Carsharing%20Optimization%20for%20BlueSG.ipynb
Read the business context & explanation here: https://karinekode.wixsite.com/folio/optimisation
Data files used are: https://github.com/karinekode/Optimisation-carsharing/blob/main/data%20-%20EV%20cars (simulation of electronic vehicles) & https://github.com/karinekode/Optimisation-carsharing/blob/main/data%20-%20trip%20durations (list of trip durations)
Gurobi package was used for optimisation
Other references:
https://www.asiaone.com/digital/2-years-bluesg-has-electric-vehicle-car-sharing-improved-singapore