Efficient reactive scheduling of multi-product batch plants under equipment failure uncertainty

In this paper, a novel formulation for short-term scheduling of multi-product batch plants under equipment failure uncertainty was presented and solved by an improved genetic algorithm. The reactive schedule problem was formulated as a mixed integer linear programming (MILP) problem using a continuous-time representation, which leads to a smaller number of binary and continuous variables thus, resulting in reduced computational times. It was shown that the proposed approach results in an efficient utilization of the plant capability as it allows the optimal selection among all rescheduling alternatives in a systematic way without the use of any heuristics. The objective function includes a term for profit maximization and a penalty term that was used to minimize the deviations from the original schedule. 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 were used to illustrate the effectiveness of the pro posed formulation and algorithm. Comparisons with other approached were provided.