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

本计画的主要目的是探讨多目标动态弹性作业车间排程问题，以降低能源消耗及其相关成本。该项目旨在开发一个系统，采用复合调度规则，包括减少能源消耗作为其主要目标。它的目的是，这样的系统可以实现在一个灵活的生产系统，其中作业调度发生在随机或不可预测的时间。所提出的调度规则将优先考虑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要求的双重挑战，这对许多企业来说可能是一个问题。在系统层面上探索降低工业制造能耗的潜力变得越来越必要，这一点迄今为止在很大程度上被忽视。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，对系统内制造系统灵活性的要求将增加，以实现大规模定制和个性化。在线决策和优化技术，以适应这些不确定性，并保持灵活的制造系统的鲁棒性，在工业4.0的背景下变得越来越重要。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潜力。通过产生快速和精英多目标调度算法，将有可能优化许多因素，如成本，能源消耗和交货时间。这些优化将大大改善制造业的现状，既减少环境足迹，又减少财务费用。大量的研究已经进行到精英主义和进化算法的发展，他们的操作是很好的理解。现有的动态调度算法将扩展到解决制造系统内的不确定性作为一个基准。通过针对这些算法的实际缺点，将有可能开发一个系统，避免这些问题，最明显的是：高计算复杂性的排序。该项目的很大一部分将集中在扩展过去的研究到多目标优化问题和进化算法。通过对制造环境中工件功能的理解，建立了车间作业调度过程的数学模型。一个专注于快速计算和实施将是这项研究的核心，以最大限度地提高调度算法的适用性，以现代制造环境。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颖博士在元计算和工业作业调度领域研究的延续。因此，刘博士的大部分研究将被审查，最终目的是将其扩展到行业应用。"

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."