Bike Sharing Systems (BSSs) offer a flexible and sustainable transport option that has gained popularity in urban areas globally. However, as users move bikes according to their own needs, imbalanced bike distribution becomes a significant challenge for BSS operators. To address this problem, we propose a Workload Awareness (WA) approach that considers the rebalancing workload of BSS sub-networks and congestion issues when repositioning bikes dynamically. Our algorithm, WA, identifies sub-networks in a BSS and ensures a similar rebalancing load for each sub-network. Our mixed integer nonlinear programming (MINLP) model then finds a repositioning policy for each sub-network, taking into account operator capacity, bike and dock information, and minimizing total losses due to bike shortages and dock congestion. Our experiments on the Ningbo City Bike system demonstrate that our approach outperforms state-of-the-art methods by reducing the loss of the system by up to 60% and significantly reducing computational time by up to 36%.