关键词: 混合流水车间, 差分进化算法, 反向学习策略, Metropolis准则, 最大完工时间

Abstract: Aiming at solving the hybrid flow shop scheduling problems, the standard DE algorithm had the disadvantages of easily falling into local extremum. Therefore, an improved DE algorithm was proposed to solve the simulation optimization model based on the minimization of makespan. The proposed algorithm was combined with the reverse learning strategy to generate the initial population, the  adaptive difference factor was further introduced into DE, and the Metropolis criterion of simulated annealing algorithm was introduced in the individual selection mechanism, which effectively improved the global search ability of the algorithm. Finally, the simulation results of the proposed algorithm and the classical algorithms were compared based on different scale examples to verify the effectiveness and superiority of the proposed improved DE algorithm.

Key words: hybrid flow-shop, differential evolutionary(DE) algorithm, reverse learning strategy, Metropolis criterion, makespan