The operator of one the world’s largest bike-sharing systems wanted a smarter way to distribute 13,000 bicycles across more than 800 stations and enable tens of thousands of journeys per day.
To make optimal use of a limited number of bikes, docking station spaces, field-workers and trucks, operators in a control room are constantly trying to work out where bikes are most likely to be needed, the optimum number of bikes to keep in each station, and the most efficient way for distribution teams to move these bikes around.
DecisionBrain used IBM® Decision Optimization to calculate the optimal number of bikes for each station at any given time, and plan efficient routes to help maintenance teams redistribute bikes accordingly.
The DecisionBrain solution is the first application of its kind that uses both optimization and machine learning to solve cycle hire inventory, distribution and maintenance problems, and could easily be re-deployed for other cycle sharing systems around the world.
Read the full case study here: Intelligent optimization of a bike sharing system helps to keep a major city on the move.
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