Gasoline blending is a key process in the successful operation of most petroleum refineries and real-time optimization (RTO) of gasoline blend recipes has the potential to provide a competitive benefit for oil refiners. The trend toward the use of "running" tanks for blender feedstocks and the recent advances in measurement technology have provided the opportunity for improved blending performance using RTO. This paper provides an improved formulation for the gasoline blend optimization problem that incorporates both the blend horizon and a stochastic model of disturbances into the RTO problem. The proposed approach is illustrated with a blender simulation study.