Research on improved genetic scheduling algorithm for double based ball flat propellant production line

Abstract:

Aiming at the problems of premature convergence and poor local search ability of traditional Genetic Algorithm(GA) in batch production process optimization, an Improved Genetic Algorithm(IGA) is designed by means of pretreatment technology. Random number method(RNM), rule generation method(RGM), and block gene interpolation method(BGIM) are used to initialize the population according to the appropriate ratio, which not only ensures the initial population diversity but also improves the individual quality. Elite retention strategies(ERS) and tournament selection strategies(TSS) are used to select individuals with good performance. A method is used to select crossover sites and take crossover operation based on the combination of location and priority obtained by expert marking, which preserves the superior genes and avoids “premature convergence”. The mutation operation is carried out via the neighborhood reorganization strategy(NRS) to ensure the generation of highquality solution populations and the inheritance of quality solutions. IGA regards maximizing the minimum delivery lead time as the objective function and realizes the scheduling algorithm research. Finally, the doublebase ball flat propellant production line is taken as an example to realize the scheduling process based on IGA, which greatly improved the order estimation efficiency of the company and the organization efficiency of the production line. The superiority of IGA is demonstrated by comparing both single and mixed initial population method.