基于改进多目标遗传算法求解混合流水车间调度问题

SOLVING HYBRID FLOW-SHOP SCHEDULING BASED ON IMPROVED MULTI-OBJECTIVE GENETIC ALGORITHM

为解决混合流水车间调度问题(HFSP),基于多目标遗传算法和粒子群算法的优点,提出一种多目标混合算法。该算法引入一种扩展的基于工序的编码,将两种算法产生的最优解分别作为彼此的初始因子,增强了遗传算法的进化速度,有效避免了粒子群算法陷入局部最优,并实现了不同加工路线的生产车间的灵活性调度。最后通过实例的数值仿真验证了算法的有效性。

Based on the advantages of multi-objective genetic algorithm and particle swarm optimisation,we proposed a multi-objective hy-brid algorithm for solving hybrid flow-shop scheduling problem （HFSP）.It introduces an extended process-based encoding,takes the optimal solutions of these two algorithms as the initial factor for each other,and speeds up the evolution of genetic algorithm as well as avoids PSO falling into local optimum,thus realises the flexible scheduling of production workshops with different processing routes.Finally,through nu-merical simulation of example we verified the effectiveness of the algorithm.