带有限等待的柔性流水车间调度问题研究
发布时间：2019-01-23 12:16
【摘要】：带等待约束的柔性流水车间调度问题广泛存在于现实的生产环境中,其调度方法研究对企业的生产管理和控制系统有着重要的影响。本文对以最小化总加权完成时间或最大完工时间为优化目标,对考虑工序间等待时间限制约束的柔性流水车间调度问题展开研究,探讨符合问题特征的求解方法。 针对以最小化总加权完工时间为目标的有限等待FFS问题,建立了整数规划模型,提出了引入惩罚函数法的混合遗传算法。惩罚函数法将迭代种群中每个个体对约束条件违反的次数进行统计,以判断不同约束的强弱地位,有利于算法在搜索前期尽快达到可行解区域,后期寻找最满意解。通过仿真软件Matlab开发调度程序,仿真结果表明该算法克服了传统遗传方法的缺点,不仅具有较强的全局收敛性,而且具有更快的寻优速度,是求解柔性流水车间调度的有效算法。 针对目标函数为最小化最大完工时间的无等待柔性流水车间调度问题,建立了混合整数规划模型,提出一种遗传算法来构造所求问题的初始解。给出了基于工件的染色体编码方法使得遗传算法的操作大大简化。对两阶段和多阶段的零等待FFS调度问题进行仿真实验,其仿真数据验证了遗传算法用于求解大型FFS调度的可行性和有效性。
[Abstract]:Flexible flow shop scheduling problem with waiting constraints widely exists in the real production environment. The study of scheduling methods has an important impact on the production management and control system of enterprises. In this paper, with the objective of minimizing the total weighted completion time or the maximum completion time, the flexible flow-shop scheduling problem with the constraint of waiting time between processes is studied, and the solution method that accords with the characteristics of the problem is discussed. For the finite wait FFS problem with the goal of minimizing the total weighted completion time, an integer programming model is established, and a hybrid genetic algorithm with penalty function method is proposed. The penalty function method counts the number of times that each individual in the iterative population violates the constraint conditions to judge the position of the different constraints, which is helpful for the algorithm to reach the feasible solution area in the early stage of searching and to find the most satisfactory solution in the later stage. The simulation results show that the algorithm overcomes the shortcomings of the traditional genetic method and not only has a strong global convergence, but also has a faster optimization speed. It is an effective algorithm to solve flexible flow shop scheduling. In this paper, a hybrid integer programming model is established for the job-shop scheduling problem with the minimum maximum completion time and the objective function is to minimize the maximum completion time. A genetic algorithm is proposed to construct the initial solution of the problem. The method of job-based chromosome coding is presented to simplify the operation of genetic algorithm. The simulation results of two-stage and multi-stage zero-wait FFS scheduling problems show that the genetic algorithm is feasible and effective in solving large-scale FFS scheduling problems.
【学位授予单位】：郑州大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TB497
本文编号：2413760
[Abstract]:Flexible flow shop scheduling problem with waiting constraints widely exists in the real production environment. The study of scheduling methods has an important impact on the production management and control system of enterprises. In this paper, with the objective of minimizing the total weighted completion time or the maximum completion time, the flexible flow-shop scheduling problem with the constraint of waiting time between processes is studied, and the solution method that accords with the characteristics of the problem is discussed. For the finite wait FFS problem with the goal of minimizing the total weighted completion time, an integer programming model is established, and a hybrid genetic algorithm with penalty function method is proposed. The penalty function method counts the number of times that each individual in the iterative population violates the constraint conditions to judge the position of the different constraints, which is helpful for the algorithm to reach the feasible solution area in the early stage of searching and to find the most satisfactory solution in the later stage. The simulation results show that the algorithm overcomes the shortcomings of the traditional genetic method and not only has a strong global convergence, but also has a faster optimization speed. It is an effective algorithm to solve flexible flow shop scheduling. In this paper, a hybrid integer programming model is established for the job-shop scheduling problem with the minimum maximum completion time and the objective function is to minimize the maximum completion time. A genetic algorithm is proposed to construct the initial solution of the problem. The method of job-based chromosome coding is presented to simplify the operation of genetic algorithm. The simulation results of two-stage and multi-stage zero-wait FFS scheduling problems show that the genetic algorithm is feasible and effective in solving large-scale FFS scheduling problems.
【学位授予单位】：郑州大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TB497
【参考文献】
相关期刊论文 前10条
1 崔建双,李铁克,张文新;混合流水车间调度模型及其遗传算法[J];北京科技大学学报;2005年05期
2 尹兆涛;李铁克;;考虑交货期和等待时间受限的HFS调度问题的混合算法[J];工业工程;2009年01期
3 王晶;姚辉;王艳亮;;有限等待流水车间调度邻域搜索算法[J];工业工程与管理;2010年03期
4 于艳辉;李铁克;;一类缓冲区有限的两阶段混合流水车间调度问题及算法[J];工业工程;2013年04期
5 李岩;李铁克;;基于约束规划的无等待混合流水车间调度问题研究[J];化工自动化及仪表;2007年03期
6 宋继伟;唐加福;;基于离散粒子群优化的轧辊热处理调度方法[J];管理科学学报;2010年06期
7 孙玲;李铁克;;基于约束传播的HFS调度研究[J];计算机工程与应用;2007年19期
8 肖拥军;李铁克;尹兆涛;;考虑特殊时间约束的混合流水车间调度[J];计算机工程与应用;2010年08期
9 张建军;王春芳;;求解混合流水车间调度问题的改进型PSO算法[J];计算机工程与应用;2011年31期
10 李霄峰;史金飞;阎威武;;混合流水车间调度的变邻域禁忌搜索算法[J];计算机工程;2008年21期
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