Adaptive genetic algorithm for two-stage hybrid flow-shop scheduling with sequence-independent setup time and no-interruption requirement.

• It addresses a class of two-stage flow shops with no-interruption requirement. • With feasibility conditions derived, an adaptive genetic algorithm is constructed. • The probability of crossover and mutation operations are adaptively adjusted. • A local search method is introduced to further improve the performance. • Extensive experiments demonstrate the efficiency and effectiveness. It is a trend for a production system to produce multiple product types with small batches. Thus, there are frequent switches between batches. Due to the different product types in different batches, setups may be necessary for a batch changeover. Based on practical applications, we investigate the problem of scheduling a class of two-stage hybrid flow shops, where there is significant setup time at the first stage, while the second stage should operate continuously without interruption. To solve this problem, after deriving the existence conditions of a feasible schedule, an adaptive genetic algorithm is designed to dynamically adjust the probabilities of crossover and mutation operations according to the population diversity. In this way, the whole solution space could be fully explored so as to get an optimal or near optimal solution for large-size practical problems. Also, based on the characteristics of solutions, a local search method is presented to further improve the solution accuracy. Experiments are done to test the efficiency and effectiveness of the proposed method. Results show that the algorithm can find good solutions with a gap of 1% with the lower bound within three minutes, i.e. , it is efficient and effective. [ABSTRACT FROM AUTHOR]