Abstract: To solve the limited-buffer flexible flow shop scheduling problem (LBFFSP), the LBFFSP’s mathematical model is established, and an improved electromagnetism-like mechanism algorithm (IEM) is proposed as the global optimizing algorithm. The random search is used in the local search strategy of standard electromagnetism algorithm, and the searching range of EM algorithm is small and it is easy to fall into the local extremum. So the idea of simulated annealing is introduced to accept the solution which makes the objective function value worse with a certain probability, which can enlarge the searching range of the algorithm, and increases the diversity of population particles, and effectively avoids the algorithm getting into the local optimal solution in the iterative search process. In addition, in order to further improve the efficiency of the algorithm for searching the optimal solution, the initial population establishment method based on optimization objective is designed to improve the quality of the initial solution of the initial population. Finally, the effectiveness of the IEM algorithm in solving the limited-buffer flexible flow shop scheduling problems is verified by comparing with SAEM algorithm and standard EM algorithm through examples tests.

Key words: Flexible flow shop, limited buffer, electromagnetism-like mechanism algorithm, simulated annealing algorithm, initial population establishment

摘要： 为了解决柔性流水车间有限缓冲区排产优化问题(Limited-Buffer flexible flow-shop scheduling problem, LBFFSP)，首先建立LBFFSP的数学模型，提出了一种改进类电磁算法(Improved Electromagnetism-like Mechanism， IEM)作为全局优化算法，由于标准类电磁算法的局部搜索采用随机线性搜索，搜索范围小，易陷入局部极值，因此引入模拟退火的思想，以一定的概率接受使目标适应度更差的解，这样可以扩大算法的搜索范围，增加种群粒子的多样性，有效避免算法在迭代过程中陷入局部极值。 另外，为进一步提高算法搜索最优解效率，设计了一种基于优化目标的初始种群建立方法，以提高初始种群中初始解的质量。最后通过实例测试，将IEM算法与SAEM算法和标准EM算法进行对比研究，验证了IEM算法对于解决柔性流水车间有限缓冲区的排产优化问题的有效性。

关键词: 柔性流水车间, 有限缓冲区, 类电磁算法, 模拟退火, 初始种群建立