Artificial Intelligence based multi-objective optimisation for energy management in dynamic flexible manufacturing systems

基于人工智能的动态柔性制造系统能源管理多目标优化

• 批准号: 2125600
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2125600
• 关键词: Artificial; Intelligence; based; multi; objective

The main goal of this project is to address the multi-objective dynamic flexible job shop scheduling problem for reducing energy consumption and its related costs. The project aims to develop a system that employs composite dispatching rules that include reduction of energy consumption as its main objective. It is intended that such a system could be implemented in a flexible production system in which job scheduling occurs at random or unpredictable times. The proposed dispatching rule would prioritise all the jobs waiting for processing on a machine in the manufacturing system while taking into account different attributes of the job and the machine, as well as time. The manufacturing industry currently faces the dual challenge of increasing energy prices, and regulatory mandates intended to reduce carbon emissions, which can prove problematic for many enterprises. It is becoming increasingly necessary to explore the potential of reducing energy consumption of industrial manufacturing at a system level, which has so far been largely ignored. At this level, operational research methods can be employed as an effective energy-saving approach. In the future, the requirement on manufacturing system flexibility within the system will be increased to realise mass customisation and personalisation. On-line decision making and optimisation techniques to accommodate these uncertainties and to maintain robustness of the flexible manufacturing system is becoming increasingly important within the background of industry 4.0. The application of multi-objective algorithms shows a great deal of promise at addressing the issues faced by the manufacturing industry. By producing fast and elitist multi-objective scheduling algorithms, it will be possible to optimise a number of factors, such as cost, energy consumption and lead time. These optimisations will go a long way in improving the current state of manufacturing, both by reducing environmental footprint and by reducing financial overheads.A great deal of research has been conducted into the development of elitist heuristics and evolutionary algorithms, and their operation is well understood. Existing dynamic scheduling algorithms will be extended to address the uncertainties within the manufacturing system as a benchmark. By targeting the practical shortcomings of these algorithms, it will be possible to develop a system which avoids these problems, most notably: the high computational complexity of the sorting.A large part of the project will focus on expanding the past research into multi-objective optimization problems and evolutionary algorithms. By developing understanding of the function of jobs within the manufacturing environment, and the implementation mathematical modelling of the job shop scheduling process. A focus on fast computation and implementation will be the heart of this research, in order to maximise the applicability of the scheduling algorithms to a modern manufacturing environment. In the context of a manufacturing environment, swift computation is of paramount importance.This project can be seen as a continuation of the research conducted by Dr. Ying Liu into the field of meta-heuristics and industrial job scheduling. As such, much of Dr. Liu's research will be examined, with the final intention of expanding it into an industry application."

本课题的主要目标是解决多目标动态柔性作业车间调度问题，以降低能源消耗及其相关成本。该项目旨在开发一个采用综合调度规则的系统，其中包括以降低能源消耗为主要目标。本发明的目的是在作业调度在随机或不可预测的时间发生的灵活生产系统中实现这种系统。拟议的调度规则将优先处理制造系统中机器上等待处理的所有作业，同时考虑作业和机器的不同属性以及时间。制造业目前面临着能源价格上涨和旨在减少碳排放的监管命令的双重挑战，这对许多企业来说可能是个问题。越来越有必要探索在系统层面上降低工业制造能耗的潜力，这一点到目前为止在很大程度上被忽视了。在这个层面上，运筹学方法可以作为一种有效的节能方法。未来，将提高系统内制造系统灵活性的要求，以实现大规模定制和个性化。在工业4.0的背景下，在线决策和优化技术以适应这些不确定性并保持柔性制造系统的健壮性正变得越来越重要。多目标算法的应用在解决制造业面临的问题方面显示出很大的前景。通过产生快速和精英的多目标调度算法，将有可能优化一些因素，如成本、能源消耗和交货期。这些优化将通过减少环境足迹和减少财务管理费用，在改善制造业现状方面大有裨益。人们对精英启发式算法和进化算法的发展进行了大量研究，它们的运作是众所周知的。现有的动态调度算法将被扩展，以解决制造系统中的不确定性作为基准。通过针对这些算法的实际缺点，将有可能开发出一个系统来避免这些问题，最显著的是：排序的计算复杂性。该项目的很大一部分将专注于将过去的研究扩展到多目标优化问题和进化算法。通过发展对制造环境中作业的功能的理解，以及实现作业车间调度过程的数学模型。为了最大限度地提高调度算法在现代制造环境中的适用性，对快速计算和实施的关注将是本研究的核心。在制造环境中，快速计算是至关重要的。该项目可以被视为刘莹博士对元启发式算法和工业作业调度领域研究的继续。因此，刘博士的大部分研究都将得到检验，最终目的是将其扩展到行业应用中。