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

大规模化工生产调度有效优化方法的开发和示范

• 批准号: EP/T03145X/1
• 负责人: JIE LI
• 金额: $ 31.22万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT03145X%2F1
• 关键词: Development; Demonstration; Effective; Optimisation; Approach

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.

英国化学工业在英国经济中起着至关重要的作用，年营业额达500亿英镑。为了在区域和全球范围内保持竞争力，英国化工公司已经转向产品定制和多样化，这反过来又导致了大量小批量，高价值的产品。此外，英国化学品制造商已开始采用灵活的多产品/多用途设施，从而提高资源利用率，降低库存成本，并更好地应对波动的制造环境。然而，由于使用了较差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。文献中现有的基于优化的大规模现实化工生产调度方法需要大量的计算成本，同时也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和工业应用的能力。本研究旨在开发一种新颖有效的基于优化的方法来解决这些挑战。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）的优势，以离线方式系统地生成鲁棒和高质量的调度规则，预计将适用于各种调度问题。与现有的基于优化的方法相比，这些高质量的调度规则将用于生成在线调度的最优或接近最优调度，从而提高利润并大大减少计算工作量。提出的解决方案方法将与工业合作伙伴Flexciton Limited在实际环境中进行测试，并将证明利润至少提高5%至20%。这项研究与之前在该领域的工作有很大的不同，它将基于新的机器学习方法和数学规划方法的结合。它将推动化学生产调度中使用优化方法的最新技术，并在解决各种大规模生产调度问题方面取得重大进展，为智能制造的研究开辟新的途径。这将有助于加强PI在这一领域的领先专业知识。它还将使英国在开发基于优化的尖端解决方案方面处于领先地位，以提高化学制造的竞争力，从而继续保持在化学工业中的领先地位。

项目成果

Novel approach to energy-efficient flexible job-shop scheduling problems

解决节能灵活作业车间调度问题的新方法

• DOI: 10.1016/j.energy.2021.121773
• 发表时间: 2021-08
• 期刊: Energy
• 影响因子: 9
• 作者: Nikolaos Rakovitis;Dan Li;Nan Zhang;Jie Li;Liping Zhang;Xin Xiao
• 通讯作者: Xin Xiao

Automatic Creation of Molecular Substructures for Accurate Estimation of Pure Component Properties using Connectivity Matrices

• DOI: 10.1016/j.ces.2022.118214
• 发表时间: 2022-10
• 期刊: Chemical Engineering Science
• 影响因子: 4.7
• 作者: Qiong Pan;Xiaolei Fan;Jie Li

Industrial Engineering in the Covid-19 Era - Selected Papers from the Hybrid Global Joint Conference on Industrial Engineering and Its Application Areas, GJCIE 2022, October 29-30, 2022

Covid-19时代的工业工程 - 工业工程及其应用领域混合全球联席会议论文选，GJCIE 2022，2022年10月29-30日

• DOI: 10.1007/978-3-031-25847-3_12
• 发表时间: 2023
• 作者: Teymourifar A

Multi-scale design of MOF-based membrane separation for CO2/CH4 mixture via integration of molecular simulation, machine learning and process modeling and simulation

• DOI: 10.1016/j.memsci.2023.121430
• 发表时间: 2023-01
• 期刊: Journal of Membrane Science
• 影响因子: 9.5
• 作者: Xinyi Cheng;Yang Liao;Zhao Lei;J. Li;Xiaolei Fan;Xin Xiao

An Open-Source Simulation Model for Solving Scheduling Problems

解决调度问题的开源仿真模型

• DOI: 10.13189/ujam.2022.100201
• 发表时间: 2022
• 期刊: Universal Journal of Applied Mathematics
• 作者: Teymourifar A