基于改进遗传算法求解柔性作业车间调度问题

Solving Flexible Job Shop Scheduling Problems Based on Improved Genetic Algorithm

针对柔性作业车间调度问题,以最小化最大完工时间、关键机器负载以及机器总负载为目标建立调度数学模型,提出一种改进改进遗传算法进行求解。算法采用两种交叉原则,通过对关键工序块操作形成二级邻域结构进行求解,并采用外部档案集对操作过程中的个体进行保留,采用加权法对个体进行评价,对Kacem基准算例进行求解,以证明所提出算法求解性能。

To solve the flexible job shop scheduling problem,a scheduling mathematical model is developed with the objectives of minimising the maximum completion time,critical machine load and total machine load,and an improved genetic algorithm is proposed for the solution.The algorithm uses two crossover principles to solve the problem by forming a secondary neighbourhood structure for the key process block operations,and uses an external archive set to retain individuals in the operation process,and uses a weighting method to evaluate individuals to solve the Kacem benchmark case to demonstrate the solution performance of the proposed algorithm.