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柔性制造系统调度的仿真英文文献和中文翻译(24)

时间:2021-08-10 19:50来源:毕业论文
5. Conclusions and Suggestions for Future Work This paper has reviewed a number of papers about the schedul- ing study of FMS by simulation, from general studies to multi- criteria approaches, and to

5. Conclusions and Suggestions for Future Work

This paper has reviewed a number of papers about the schedul- ing study of FMS by simulation, from general studies to multi- criteria approaches, and to AI approaches.  From  Tables 1, 3, and 5, it was found that most authors considered the parts dispatching scheduling problem and the trend is consistent for all three categories under review. In addition, as illustrated in Tables 2 and 4, flow-time and  tardiness  related  measures are the most frequently applied performance measures to reflect system  status.  This is,  in  fact,  a  representation  of  a decision

maker’s objectives in reality, who would like to allocate the greatest effort to shorten lead time and produce goods on sched- ule.

Off-line simulation of an existing or imaginary  system  is very popular in the published work. The majority of the papers dealt with this methodology. However, a physical model or simulator, like [60] and [65], for a real-time model is worth considering  in  future work.

We may see a future trend of study in AI approaches. Both simulation and AI techniques are regarded inpidually as flexible tools for modelling and analysis. In addition, AI tech- niques possess learning ability, which is lacking in traditional scheduling rules. In this connection, if they  were  combined as an integrated tool, this could be a very powerful tool capable of handling a larger variety and unpredictable  situations  of FMS scheduling problems. However, it seems that no one has attempted to use hybrid AI techniques for analysing scheduling problems in FMS. Since different AI approaches would have different learning capabilities, work in this area would be valuable.

柔性制造系统的调度(FMSs)对研究员和从业者来说是最有吸引力的领域之一。自从20世纪70年代末期,纸的批量生产被第一次发表以来,该领域的相关文献开始急剧增加。许多新手段被采纳用于规划FMSs,包括仿真技术和解析方法。针对任意一种手段都能找到数量众多的文章。由于仿真是造型FMSs中最广泛使用的工具,这篇调查主要评论了采用仿真技术作为分析工具的FMSs时序研究。调度方法学被分为综合调度研究仿真、多标准调度途径以及FMSs领域中人工智能(AI)的应用。出版物上的评论以及未来研究发展的建议已被提出。

关键词:人工智能(AI);柔性制造系统;多标准;调度;仿真

 

1. 介绍

1.1 FMSs的介绍

制造业最早作为组织成功的重要关键点出现于20世纪90年代,许多综合性制造业战略作为制造业手段[1]复兴的结果得到了广泛的关注。计算机集成制造业、准时制生产方式(JIT)制造业、工厂自动化、工业倾斜、柔性制造系统(FMSs)就是再现的主题中的一部分。

对制造业发展有着积极作用的最大单一因素是FMSs的引进。FMSs是对大批量生产的需求和对产品质量的顾虑不断增长所产生的成果。另一个主要诱因是基于对制造工业比过去更快速变化[2]的响应的需求。

FMSs可以以各种方法下定义,但迄今为止并没有一种被作为标准接收的柔性制造系统综合术语的定义。大多数定义都是基于系统中使用的硬件。例如,Byrkett等人[3]这样描述:

一个柔性制造系统是指在计算机控制下成组的数控机器(机器中心)和一个材料处理系统共同工作的制造系统。

O’Keefe和Kasirajan[4]把FMSs定义为:论文网

在计算机集中控制下一组被材料处理系统(MHS)联结在一起用于生产或装配许多不同部件型号的工作站。

还有的定义是基于系统的性能和表现。例如,Kaltwasser等人[5]这样描述: 柔性制造系统调度的仿真英文文献和中文翻译(24):http://www.751com.cn/fanyi/lunwen_79959.html

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