摘要
研究了含串行批处理机的多阶段柔性流水车间调度问题,其中,第一阶段有多台串行批处理机而其他阶段为离散机,考虑工件在各加工阶段间的运输时间,以最小化总加权完成时间为目标建立数学模型。在常规遗传算法的基础上,设计遗传参数使其随遗传代数和适应函数值进行自适应调节,结合顺序交叉策略,提出改进的遗传算法以求解该NP难题。通过仿真软件Matlab开发调度程序实现上述算法,测试结果表明,与常规遗传算法相比,所提出算法能在较短的时间内得到更好的解;与拉格朗日松弛算法相比,求解中大规模问题时,改进遗传算法在计算时间和解的质量方面的优势较为明显。
The problem of scheduling n jobs in a multi-stage flexible flowshop with serial batch production at the first stage is studied. The first stage consists of multiple serial batching machines in parallel and the other stages contain discrete machines. A mathematical model is formulated to minimize the total weighted completion time with the consideration of transportation time among the adjacent processing stages. An improved genetic algorithm is developed based on ordered crossover and adaptive adjustment of genetic parameters for this NP-hard problem where the genetic parameters are associated with the numbers of the iteration and the values of the fitness function. The above algorithm is performed using the simulation software Matlab. Testing results show that the proposed algorithm can find the better solutions within a shorter period of time, as compared with the general genetic algorithm. The comparison with Lagrangian relaxation shows that the improved genetic algorithm performs better on the computation time and solution quality for medium and large sized problems.
作者
轩华
王君妍
王薛苑
XUAN Hua;WANG Jun-yan;WANG Xue-yuan(School of Management Engineering,Zhengzhou University,Zhengzhou 450001,China)
出处
《控制工程》
CSCD
北大核心
2018年第8期1415-1420,共6页
Control Engineering of China
基金
教育部人文社会科学研究项目(15YJC630148)
国家自然科学基金(71001091,71001090,U1604150)
中国博士后科学基金(2014T70684,2013M531683)
郑州大学优秀青年教师发展基金(1421326092)
河南省高等学校重点科研项目(17A520058)
关键词
柔性流水车间调度
串行批处理
改进遗传算法
顺序交叉
自适应调节
Flexible flowshop scheduling
serial batch production
improved genetic algorithm
ordered crossover
adaptive adjustment
