  Following the growth of dock-based bike sharing systems as an eco-friendly
solution for transportation in urban areas, Dockless systems are
revolutionizing the market for the increased flexibility they offer to users.
Bike redistribution is a common approach to improve service, and there exists
extensive research considering static and dynamic rebalancing strategies for
dock-based systems. We approach the dockless problem by defining abstract
stations from trip start and end location frequency. This paper offers an
optimizing Mixed Integer Program framework to model the effects of various bike
repositioning strategies for dockless systems. We process 30 days worth of
Singapore-based dockless bike data from September 2017 to extract trips.
Pairing our mixed integer program with a demand model built from the processed
data, we unveil trends between fleet size, lost demand, and magnitude of
repositioning proper to the repositioning strategy employed. We also show that
increasing repositioning potential does not always improve service performance.
