带批处理机的多阶段柔性流水车间调度优化-《郑州大学学报(工学版)》

文章信息/Info

Author(s):: Wang Junyan; Wang Xueyuan; Xuan Hua; School of Management Engineering, Zhengzhou University, Zhengzhou, Henan 450001

Keywords:: flexible flowshop scheduling; batch machines; total weighted completion time; self-adaptive genetic algorithm; self-adaptive adjustment

摘要:: 从钢铁行业的炼钢—连铸—热轧过程提炼出中间阶段有多台批处理机,其它阶段为离散机的多阶段柔性流水车间调度问题.首先,结合工件动态到达、各阶段间的运输时间以及机器的调整时间等生产特征,对问题进行描述,建立以最小化总加权完成时间为目标的数学模型.然后,针对该问题提出了改进的自适应遗传算法,使遗传参数随其迭代及适应函数值调节.对150个工件的大量随机数据进行测试,结果表明,与常规遗传算法相比,所提出的自适应遗传算法能在较短的计算时间内得到更好的解;与拉格朗日松弛算法相比,求解大规模问题时,所提算法在解的质量方面优势较为明显.

Abstract:: Based on steel making-continuous casting-hot rolling production process in iron and steel industry,the problem of scheduling n jobs in a multi-stage flexible flowshop with batching machines at some middle stage was studied.The batching production stage consisted of multiple serial batching machines in parallel,and the other stages contained discrete machines.Firstly,a mathematical model was formulated to minimize the total weighted completion time withthe consideration of job dynamic arrival,transportation time between the adjacent stages and machine setup time.Then,an improved adaptive genetic algorithm was developed for this NP-hard problem where the genetic parameters were associated with the iteration number and the fitness function values.Computational experiments tested a large number of random data for up to 150 jobs.The results show that the proposed algorithm could find the better solutions within a shorter period of time,as compared with the general genetic algorithm.The comparison with Lagrangian relaxation showed that the improved genetic algorithm performed better on solution quality for medium and large sized problems.