Uncertainty-Aware Planning and Scheduling in the Process Industries

由于全球生产和客户基础的扩大，工艺规划和调度问题变得越来越复杂。决策者不断面临着在不确定性条件下优化生产计划和降低成本的挑战。不确定性可归因于各种因素，包括不稳定的客户需求、过程性能的变化、生产工厂所在地周围社会经济的波动等。另一个复杂的问题是必须作出和执行决定的时间尺度。如果不能有效地考虑到这些问题，可能会导致成本增加、客户不满、竞争优势的丧失，最终导致制造基地的关闭。本项目旨在开发考虑多个时间尺度的不确定条件下最优决策的计划和调度工具。每个过程规划和调度问题都是独特的，因此一个建模和模型解决方案工具无法解决每个问题的特殊性。将不确定性划分为特定类别的框架是提供前沿最优解决方案的关键。因此，一个问题将有许多不确定因素，这些不确定因素将根据我们提出的框架进行分类，然后为每个分类调用适当的解决方案方法。还将开发一种混合不确定性建模和优化工具，利用各种不确定性解决方案技术的协同作用。本项目开发的新型计划和调度工具将在来自各种行业的过程工业的实际案例研究中进行测试，包括能源系统、农用化学品、制药、消费品、石油和天然气以及工业气体。将得到基于个性化不确定性的最优规划和调度方案。

Process planning and scheduling problems are becoming increasingly complex due to the expanding production and customer base around the globe. A decision maker is continuously faced with the challenge to optimise the production plans and reduce costs under uncertainty. The uncertainty can be attributed to factors including volatile customer demands, variations in the process performance, fluctuations in socio-economics around the locations of the production plants, etc. Another complicating issue is the time-scale at which the decisions have to be taken and implemented. Not being able to effectively take these issues into account can lead to increased costs, customer dissatisfaction, loss of competitive edge and eventually shutting down of the manufacturing bases. This project aims to develop planning and scheduling tools for optimal decision-making under uncertainty while taking into account the multiple time-scales. Each process planning and scheduling problem is unique and hence one modelling and model solution tool cannot address the peculiarities of each problem. A framework where uncertainties are classified into specific categories is the key to providing cutting-edge optimal solutions. So, a problem will have a number of uncertainties which will be classified based upon our proposed framework and then for each classification the appropriate solution methodology will be invoked. A hybrid uncertainty modelling and optimisation tool that exploits the synergies of the solution techniques for various classes of uncertainty will also be developed. The novel planning and scheduling tools developed in this project will be tested on real-life case studies from process industries from a wide variety of sectors including energy systems, agrochemicals, pharmaceuticals, consumer goods, oil & gas, and industrial gases. Optimal planning and scheduling solutions based upon personalised uncertainty will be obtained.