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

英国化学工业在英国经济中扮演着至关重要的角色，年营业额总计500亿GB。为了在地区和全球范围内保持竞争力，英国化工公司已转向产品定制化和多样化，这反过来又导致了大量低产量、高价值的产品。此外，英国化学品制造商已经开始采用灵活的多产品/多用途设施，允许更高的资源利用率、更低的库存成本和对不断变化的制造环境更好的响应。然而，由于使用了糟糕的启发式基于规则的生产调度方法，这些优势并没有完全实现，这可能导致该部门损失估计在数亿英镑的潜在年利润。现有的基于优化的大规模实际化工生产调度方法需要巨大的计算成本，并且难以提供最优或接近最优的解决方案，这限制了它们实现上述优势的能力和工业应用。本研究旨在开发一种新颖而有效的基于优化的方法来应对这些挑战。它将结合数学规划方法和一种新的机器学习技术--基因表达式编程(GEP)的优点，以离线的方式系统地生成健壮和高质量的调度规则，有望适用于各种调度问题。这些高质量的调度规则将被用来以在线方式生成最优或接近最优的调度计划，与现有的基于优化的方法相比，具有更高的利润和显著减少的计算工作量。所提出的解决方法将在实际环境中与工业合作伙伴Flexiton Limited进行测试，利润将至少提高5%到20%。这项研究与以前在这一领域的工作有很大不同，因为它将基于新的机器学习方法和数学规划方法的结合。它将推动在化工生产调度中使用最优化方法的最先进水平，并导致在解决各种大规模生产调度问题方面取得重大进展，开辟智能制造研究的新途径。这将有助于加强国际和平协会在这一领域的领先专业知识。它还将使英国在开发尖端的基于优化的解决方案方法方面处于领先地位，以提高化工制造的竞争力，从而继续保持化工行业的领先地位。

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.