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

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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."