A novel parallel multi-objective genetic algorithm for process industry production scheduling

This paper analyzes the features of production scheduling problems in process industries which are different from general manufacturing systems. A parallel multi-objective genetic algorithm is proposed based on the distinctive characteristics of process scheduling, such as multiple objectives, hybrid dynamics and real time computation. An objective ranking evaluation technique is developed to associate the trade-off information to a better solution with preference articulation. A novel double layer chromosome coding method is used to express the system hybridness. Computation time is at least reduced to 10 percent of its original value by adopting a hierarchical decomposed parallel computing technique. Simulation results show that the algorithm illustrated in this paper has the prospect of future use in applications to such complex optimization problems in process scheduling.