基于改进遗传算法的车间调度问题研究

Research on Job Shop Scheduling Problem Based on Improved Genetic Algorithm

车间调度将直接影响企业的制造成本、交货日期和生产效率等,对其生产调度研究具有重要意义。首先,利用改进遗传算法建立以最小化最大生产周期为目标的适应度函数;其次,通过染色体编码计算每个个体的适应度函数,将每一代中最优个体保留下来直接遗传给下一代,其余个体根据适应度函数进行选择,保证最优个体不会丢失以及种群的多样性特征,同时利用两点交叉和逆转变异概率解决作业车间调度问题;最后,通过仿真结果表明了改进遗传算法解决这类车间调度问题的高效性和可行性。

Workshop scheduling will directly affect the manufacturing cost,delivery date,production efficiency,etc.of the company,which is of great significance for its production scheduling research.In this paper,we first use the improved genetic algorithm to establish the fitness function aiming at minimizing the maximum production cycle.Secondly,through the chromosome coding,calculate the fitness function of each individual,and retain the optimal individual in each generation and directly pass it to the next generation.The rest of the individuals choose according to the fitness function to ensure that the optimal individual will not be lost and the diversity characteristics of the population.At the same time,the two-point intersection and the inverse transformation probability are used to solve the job shop scheduling problem.Finally,the simulation results show that the improved genetic algorithm solves this problem.The efficiency and feasibility of the class shop scheduling problem.