In this paper, a novel formulation for the short-term scheduling of multiproduct batch plants under demand uncertainty is presented. Then it is solved by an improved genetic algorithm. The proposed approach results in an efficient utilization of the plant capability as it allows the optimal selection among all the rescheduling alternatives in a systematic way without the use of any heuristics. Moreover, the objective function can not only maximize the total profit of the plant and minimize the makespan but also allow the flexibility for modeling different weighted instances of the two targets so that a best-possible decision can be determined. According to the discrete characteristic of scheduling of batch plants, through the improvement of the coding method, an effective genetic algorithm is presented. Two examples are given to illustrate the effectiveness of the proposed formulation and algorithm.