  As a new generation of Public Bicycle-sharing Systems (PBS), the dockless PBS
(DL-PBS) is an important application of cyber-physical systems and intelligent
transportation. How to use AI to provide efficient bicycle dispatching
solutions based on dynamic bicycle rental demand is an essential issue for
DL-PBS. In this paper, we propose a dynamic bicycle dispatching algorithm based
on multi-objective reinforcement learning (MORL-BD) to provide the optimal
bicycle dispatching solution for DL-PBS. We model the DL-PBS system from the
perspective of CPS and use deep learning to predict the layout of bicycle
parking spots and the dynamic demand of bicycle dispatching. We define the
multi-route bicycle dispatching problem as a multi-objective optimization
problem by considering the optimization objectives of dispatching costs,
dispatch truck's initial load, workload balance among the trucks, and the
dynamic balance of bicycle supply and demand. On this basis, the collaborative
multi-route bicycle dispatching problem among multiple dispatch trucks is
modeled as a multi-agent MORL model. All dispatch paths between parking spots
are defined as state spaces, and the reciprocal of dispatching costs is defined
as a reward. Each dispatch truck is equipped with an agent to learn the optimal
dispatch path in the dynamic DL-PBS network. We create an elite list to store
the Pareto optimal solutions of bicycle dispatch paths found in each action,
and finally, get the Pareto frontier. Experimental results on the actual DL-PBS
systems show that compared with existing methods, MORL-BD can find a higher
quality Pareto frontier with less execution time.
