Development and Demonstration of an Effective Optimisation Approach for Large-scale Chemical Production Scheduling

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The UK chemical industry plays a vital role in the UK economy with a total annual turnover of £50 billion. To remain competitive both regionally and globally, UK chemical companies have moved towards product customisation and diversification, which in turn have resulted in a large number of low-volume, high-value products. Furthermore, UK chemical manufacturers have started to employ flexible multiproduct/multipurpose facilities, which allow for higher utilisation of resources, lower inventory costs and better responsiveness to a fluctuating manufacturing environment. However, these advantages have not been fully achieved due to the use of poor heuristic rule-based production scheduling methods, which could cause the sector to lose potential annual profits estimated in the hundreds of millions of pounds.Existing optimisation-based methods for large-scale real-world chemical production scheduling in the literature require significant computational cost while also struggling to provide optimal or near-optimal solutions, which restrict their capability to achieve the aforementioned advantages and industrial application. This research is to develop a novel and effective optimisation-based method to address these challenges. It will combine the advantages of the mathematical programming approach and a new machine learning technique, Gene Expression Programming (GEP), for systematic generation of robust and high-quality dispatching rules in an offline manner, which are expected to be applicable for a variety of scheduling problems. These high-quality dispatching rules will then be used to generate optimal or near-optimal schedules for scheduling in an online manner with improved profit and substantially reduced computational effort when compared to existing optimisation-based methods. The proposed solution approach will be tested in a practical context with the industrial collaborator Flexciton Limited and an improvement in profit of at least 5% and up to 20% will be demonstrated.This research is significantly different from previous work in this area in that it will be based upon the combination of the new machine learning method and the mathematical programming approach. It will advance the state of the art in the use of optimisation methodologies in chemical production scheduling and lead to significant advances in solving a variety of large-scale production scheduling problems, opening new avenues of research in smart manufacturing. It will help strengthen the leading expertise of the PI in this field. It will also allow the UK to take a leading position in developing the cutting-edge optimisation-based solution approach to improve chemical manufacturing competitiveness and thus continue to remain the leading position in chemical industries.