The paper presents a unified model for handling dose constraints (physical dose, equivalent uniform dose (EUD), etc.) and radiation source constraints in intensity-modulated radiation therapy (IMRT) within a mathematical framework based on the split feasibility problem. The model does not impose an exogenous objective function on the constraints but instead minimizes a weighted proximity function that measures the sum of the squares of the distances to the constraint sets. This ensures convergence to a feasible solution if the split feasibility problem is consistent, or to a solution that minimally violates the constraints otherwise. The authors develop an optimization algorithm and present computational results demonstrating the validity of the model and the effectiveness of the proposed algorithmic scheme. The model can be extended to include other dose constraints, such as dose–volume constraints. The paper also discusses the use of projection algorithms to find points that satisfy the constraints and the implementation of the algorithm in a clinical case study.The paper presents a unified model for handling dose constraints (physical dose, equivalent uniform dose (EUD), etc.) and radiation source constraints in intensity-modulated radiation therapy (IMRT) within a mathematical framework based on the split feasibility problem. The model does not impose an exogenous objective function on the constraints but instead minimizes a weighted proximity function that measures the sum of the squares of the distances to the constraint sets. This ensures convergence to a feasible solution if the split feasibility problem is consistent, or to a solution that minimally violates the constraints otherwise. The authors develop an optimization algorithm and present computational results demonstrating the validity of the model and the effectiveness of the proposed algorithmic scheme. The model can be extended to include other dose constraints, such as dose–volume constraints. The paper also discusses the use of projection algorithms to find points that satisfy the constraints and the implementation of the algorithm in a clinical case study.