AIOLOS: Artificial Intelligence powered framework for OnLine prOduction Scheduling

AIOLOS：人工智能驱动的在线生产调度框架

• 批准号: EP/V051008/1
• 负责人: JIE LI
• 金额: $ 106.18万
• 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: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV051008%2F1
• 关键词: AIOLOS; Artificial; Intelligence; powered; framework

The chemical industry in the UK plays a vital role in the nation's economy with a total annual turnover of £50 billion. To remain competitive both regionally and globally, optimisation-based scheduling methods are often applied to achieve a significant increase in process profit, reduction in energy cost, improvement in the efficiency of inventory management, and enhanced customer satisfaction. However, frequent disruptions such as demand fluctuation, rush order arrivals, due date changes, and equipment malfunction are unavoidable in chemical manufacturing. When these disruptions are present, a pre-determined optimal schedule can become suboptimal or even infeasible. With the use of heuristic-based reactive scheduling methods in response to frequent disruptions, the UK chemical industry loses an estimated profit in the order of hundreds of millions of pounds every year. The existing optimisation-based scheduling methods either require high computational expense to generate a schedule, thus rendering them incapable of managing unexpected disruptions in online scheduling; or directly use poor heuristics or knowledge for fast decision-making which usually leads to a conservative schedule resulting in significant financial losses. More importantly, these methods cannot effectively accommodate certain disruptions such as equipment malfunction and rush order arrivals that often occur in online scheduling, restricting their potential application.This research will deliver a next generation autonomous online scheduling framework in response to different types of disruptions in the chemical manufacturing industry. The framework will generate high-quality dispatching rules to provide optimal or near-optimal online scheduling solutions for emerging uncertainties in a timely manner (e.g., < 5 minutes) through integration of novel machine learning techniques and robust mathematical programming approaches. This will also allow for the identification of a solution to minimise energy consumption. The research will be addressed via a seamless collaboration between The University of Manchester and University College London with expertise in process systems engineering and machine learning. The proposed framework will be tested in close interactions with industrial partners in the UK and China. The improvement in profit is expected to be at least 3% and potentially up to 15%, corresponding to an estimated annual increase in profit between £70 million and £320 million for the UK chemical industry.

英国的化学工业在国家经济中发挥着至关重要的作用，年营业额为500亿英镑。为了保持区域和全球的竞争力，通常应用基于优化的调度方法来实现过程利润的显著增加、能源成本的降低、库存管理效率的提高以及客户满意度的提高。然而，在化工生产过程中，需求波动、紧急订单到达、交货期变更和设备故障等频繁的中断是不可避免的。当这些中断存在时，预先确定的最优调度可能变得次优甚至不可行。随着使用基于敏捷的反应调度方法来应对频繁的中断，英国化学工业每年损失的利润估计高达数亿英镑。现有的基于优化的调度方法要么需要很高的计算费用来生成一个时间表，从而使他们无法管理意外的中断在线调度;或直接使用穷人的知识或快速决策，这通常会导致一个保守的时间表，导致重大的财务损失。更重要的是，这些方法不能有效地适应某些中断，如设备故障和紧急订单的到来，经常发生在在线调度，限制其潜在的应用。该框架将生成高质量的调度规则，以便及时为新出现的不确定性提供最佳或接近最佳的在线调度解决方案（例如，< 5分钟），通过整合新颖的机器学习技术和强大的数学编程方法。这也将允许识别解决方案，以最大限度地减少能源消耗。该研究将通过曼彻斯特大学和伦敦大学学院之间的无缝合作来解决，并在过程系统工程和机器学习方面拥有专业知识。拟议的框架将在与英国和中国的工业合作伙伴的密切互动中进行测试。利润的改善预计至少为3%，可能高达15%，相当于英国化学工业的利润估计每年增加7000万英镑至3.2亿英镑。

项目成果

Stable optimisation-based scenario generation via game theoretic approach

• DOI: 10.1016/j.compchemeng.2024.108646
• 发表时间: 2023-11
• 期刊: Comput. Chem. Eng.
• 作者: Georgios L. Bounitsis;L. Papageorgiou;Vassilis M. Charitopoulos

Data-driven scenario generation for two-stage stochastic programming

• DOI: 10.1016/j.cherd.2022.08.014
• 发表时间: 2022-08
• 期刊: Chemical Engineering Research and Design
• 影响因子: 3.9
• 作者: Georgios L. Bounitsis;L. Papageorgiou;Vassilis M. Charitopoulos

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

33rd European Symposium on Computer Aided Process Engineering

第33届欧洲计算机辅助过程工程研讨会

• DOI: 10.1016/b978-0-443-15274-0.50255-9
• 发表时间: 2023
• 作者: Marousi A

Learning and Intelligent Optimization - 17th International Conference, LION 17, Nice, France, June 4-8, 2023, Revised Selected Papers

学习与智能优化 - 第 17 届国际会议，LION 17，法国尼斯，2023 年 6 月 4-8 日，修订后的精选论文

• DOI: 10.1007/978-3-031-44505-7_2
• 发表时间: 2023
• 作者: Liapis G