Abstract: Aiming at the hybrid flow-shop scheduling problem with lot-streaming(HFSP_LS), an improved genetic algorithm(IGA) with adaptive crossover probability and mutation probability is designed. Firstly, the mathematical model of batch scheduling in hybrid flow shop is established by minimizing the maximum completion time as the objective function. Secondly, the matrix encoding and decoding strategies for the first phase are designed according to the first come first serve(FCFS) principle. Then, the global search capability of the algorithm is enhanced by integrating the selective operation of elite retention strategy and roulette method, adaptive crossover probability and multi-fragment crossover operation, adaptive mutation probability and multi-fragment mutation operation. Finally, the effectiveness and superiority of the proposed algorithm are verified by case test and algorithm comparison.
Keywords: hybrid flow shop; batch scheduling; improved genetic algorithm(IGA); genetic operation; elite retention strategy