OBJECTIVE Adaptive radiation therapy (ART) can reduce normal tissue toxicity and/or improve tumor control through treatment adaptations based on the current patient anatomy. Developing an efficient re-planning algorithm is an important step for ART. For the re-planning process, manual trial-and-error approach to fine-tune planning parameters is a time- consuming and resource-requiring task. However, prior information in the original plan, such as dose distribution or dose-volume histogram (DVH) can be employed to facilitate the re-planning. The goal of this work is to develop a re-planning algorithm that automates voxel weighting factor adjustments to generate a plan with close, or possibly better, DVH curves compared with original plan. METHODS Our algorithm iterates the following two loops. The inner loop is the traditional fluence map optimization, in which we optimize a quadratic objective function penalizing the deviation of the dose received by each voxel from its prescribed or threshold dose with a set of fixed weighting factors. In outer loop, the weighting factors in the objective function for each voxel are heuristically adjusted according to the deviation of the DVH curves in the calculated plan from those in the original plan. The process is repeated until the result converges, or the maximum iteration step is reached. The whole algorithm is implemented on GPU for high efficiency. RESULTS We have tested our algorithm on three 8-field head-and-neck cases. Compared with the DVH curves in original plan, the DVH curves in the resulting plan using our algorithm with 30 iterations are almost better for every structure. We can finish the re-optimization process around 30 seconds. CONCLUSIONS Adjusting voxel-based weighting factors automatically, by comparing the DVH curves, seems to be a promising approach to avoid the tedious trial-and-error scheme for ART re-planning. This work is supported by Varian Medical Systems through a Master Research Agreement.