The imbalanced distribution of shared bikes in the dockless bike-sharing system (a typical example of the resource-sharing system), which may lead to potential customer churn and lost profit, gradually becomes a vital problem for bike-sharing firms and their users To resolve the problem, we first formulate the bike-sharing system as a Markovian queueing network with higher-demand nodes and lower-demand nodes, which can provide steady-state probabilities of having a certain number of bikes at one node A model reduction method is then designed to reduce the complexity of the proposed model Subsequently, we adopt an operator-based relocation strategy to optimize the reduced network The objective of the optimization model is to maximize the total profit and act as a decision-making tool for operators to determine the optimal relocation frequency The results reveal that it is possible for most of the shared bikes to gather at one low-demand node eventually in the long run under the influence of the various arrival rates at different nodes However, the decrease of the number of bikes at the high-demand nodes is more sensitive to the unequal demands, especially when the size of the network and the number of bikes in the system are large It may cause a significant loss for operators, to which they should pay attention Meanwhile, different estimated values of parameters related with revenue and cost affect the optimization results differently