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

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.

The potential impact of the research includes the impact on industry, the economy and society, knowledge, people and policy, all of which is summarised below.

1. Impact on industry
The UK chemical industry loses a significant profit (approximately hundreds of millions of pounds) every year largely due to the use of the 'trial and error' simulation scheduling methods or the poor heuristic rules based on the operator's experience, which are utilised in an attempt to find a feasible operating schedule. The effective optimisation-based solution approach developed in the research will identify an optimal or near-optimal solution, which is expected to lead to an improvement in profit of at least 5%, and up to 20%, with zero capital expense (an estimated annual increase in profit from £90 million to £450 million), higher resource utilisation, lower inventory costs and better responsiveness to a fluctuating manufacturing environment. This will help the chemical industry to improve its competitiveness and enhance its current leading position.

2. Impact on the economy and society
The expected improvement in profit through the proposed solution approach in the research will be at least 5%, and up to 20%, with zero capital expense (an estimated annual increase in profit from £90 million to £450 million for the UK chemical industry, which would add an estimated additional value of £800 million to the UK economy annually). This will in turn help create more employment opportunities in the sector, provide more training and improve welfare for society, contributing to a better quality of life in the UK.

3. Impact on knowledge
This research will advance the state of the art in the use of optimisation methodologies in process operations, opening a new avenue of research in process systems engineering. The results from this research will be published openly in high-impact journals and also disseminated at seminars and leading national and international conferences. The results of this research will in addition be used to develop educational materials for a graduate-level course that PI teaches.

4. Impact on people
This research will help develop a future generation of leading academics in the UK through training of the post-doctoral research associate (PDRA) in the field of process modelling, machine learning, and optimisation. The high-level skills and expertise gained from this research will enable the PDRA to make valuable contributions in both industry and academia in key areas that suffer from significant shortages of highly-skilled professionals. The expertise of the PI in this field will be strengthened through high-impact journal publications and presentations at leading national and international conferences. The developed mathematical models and solution approach will be of interest to academics and researchers, particularly those working in the field of process scheduling. Both the code and the library of problems developed during this research will be made publicly available so that other academics and researchers in this field can replicate (or improve upon) the results. This research will also offer the PI an opportunity to further develop his MSc and MEng research program, which allows MSc and MEng students to use some of the cyber-infrastructure tools developed in this effort to carry out computational studies and receive training for optimisation-based chemical production scheduling.

5. Impact on policy
The optimal strategies for scheduling of flexible chemical facilities in the chemical industry can be used for the creation of scheduling decision policy. They can also help better understand existing decision policies, challenges and the needs in developing new scheduling policies. These insights will be shared with relevant policymakers who will be engaged in different ways to ensure that any policy recommendations are relevant and effective.