摘要
随着半导体产业的快速发展产生了很多重要的生产计划问题,其中,TFT-LCD(薄膜晶体管液晶显示器)属于资金和技术密集型产业,面对激烈的市场竞争亟需提高生产力.提出一种改进灰狼优化算法求解以最小化最大完工时间为优化目标的TFT-LCD模块组装调度问题,针对该问题特点对基本灰狼优化算法进行了一系列改进,包括工序插入式方法解码,机器选择部分采用一种全局搜索、局部搜索和随机产生相结合的初始化方法,基于搜索的方法进行工序排序部分初始化,以及均匀交叉操作和进化种群动态操作.同时,对所设计的改进灰狼优化算法的计算复杂度和收敛性进行了分析.由于该问题与柔性作业车间调度问题(FJSP)比较相似,通过对FJSP问题的不同规模基准算例的仿真实验,验证了算法有效性.另外,通过对实际生产活动中的一个TFT-LCD模块组装调度问题的测试,进一步表明本文提出的算法解决真实TFT-LCD模块组装调度问题的实用性和有效性.
The semiconductor industry has grown rapidly and subsequently production planning problems have raised many important research issues. TFT-LCD manufacturing is a capital and technology intensive industry. Facing the fierce competitive pressures, it is important to enhance productivity. An improved grey wolf optimizer （ IGWO） was proposed in this paper to solve the TFT-LCD module assembly scheduling problem with the makespan criterion. Considering the characteristics of the problem, the left-shift-based decoding was used to generate an active schedule. In generation of initial population, an initalizafion method which combined with global search, local search and random generation was designed for the machines selection part, and a search-based initialization method was developed for the operations sequencing part,respectively. Uniform crossover operator and evolutionary population dynamics （EPD） method were also employed. Meanwhile, we provided the computation complexity analysis and convergence analysis of the proposed IGWO algorithm. As the TFT-LCD module assembly production is a flexible job-shop scheduling problem, numerical experiments and comparisons based on a set of benchmark instances from Kacem data and BRdata demonstrate the validity of this approach. Furthermore, the proposed IGWO was used to solve a real-world TFT-LCD module assembly scheduling case and its applicability is verified.
作者
姚远远
叶春明
杨枫
YAO Yuan-yuan;YE Chun-ming;YANG Feng(Business School,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2018年第10期2146-2153,共8页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(71271138)资助
上海理工大学科技发展项目(16KJFZ028)资助
上海市高原学科项目"管理科学与工程"项目(GYXK1201)资助
