Dockless bike-sharing systems with unusable bikes: removing, repair and redistribution under batch policies

This paper discusses a large-scale dockless bike-sharing system (DBSS) with unusable bikes, which can be removed, repaired, redistributed and reused under two batch policies: one for removing the unusable bikes from each parking region to a maintenance shop, and the other for redistributing the repaired bikes from the maintenance shop to some suitable parking regions. For such a bike-sharing system, this paper proposes and develops a new computational method by applying the RG-factorizations of block-structured Markov processes to the closed queueing networks. Different from previous works in the literature of queueing networks, a key contribution of our computational method is to set up a new nonlinear matrix equation to determine the relative arrival rates, and to show that the nonlinearity comes from two different groups of processes: The failure and removing processes, and the repair and redistribution processes. Once the relative arrival rate is introduced to each node, these nodes are isolated from each other so that the Markov processes of all the nodes are independent of each other, and thus the Markov system of each node is described as an elegant block-structured Markov process whose stationary probabilities can be easily computed by the RG-factorizations. Based on this, the paper establishes a more general product-form solution of the closed queueing network, and provides performance analysis of the DBSS through a comprehensive discussion of the bikes' failure, removing, repair, redistribution and reuse processes under two batch policies. We hope that our method opens a new avenue for quantitative evaluation of more general DBSSs with unusable bikes.

Automated summary

This paper discusses a large-scale dockless bike-sharing system (DBSS) with unusable bikes and proposes a new computational method based on the RG-factorizations of block-structured Markov processes to analyze the system's performance under two batch policies. The method sets up a nonlinear matrix equation to determine arrival rates and establishes a more general product-form solution for the closed queueing network.