Uncertainty-Aware Planning and Scheduling in the Process Industries

Lead Research Organisation: University College London
Department Name: Chemical Engineering


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.

Planned Impact

Economic Impact. Chemical and process industry make a major contribution to the UK economy. To safegaurd this revenue and ensure growth in this sector we will work with all the major process industries including agro-chemicals, pharmaceuticals, fast moving consumer goods (FMCG), oil and gas, and industrial gases. For these industries to continue making profit in the short and the long term they need to stay ahead of the game by planning so that any uncertainties in the future are taken into account while making the decisions now. This project will develop new computational tools that will immunise the profitability of the process industry to the uncertainties, by developing a hybrid modelling and optimisation framework for different categories of uncertainties. We will also use the channels of our CPSE Industrial Consortium and the prototype software to increase the uptake of the project outcomes by the process industry.

Social Impact. The direct benefit to the society will be the security in the supply of energy, drugs, food and other consumer goods. The indirect benefits will come from the economic prosperity leading to better standards of living and reduction in the number of unemployed people. The process industry will also be able to engage more proactively with the governmental and financial organisations to influence policy makers based upon the planning policy solutions obtained from the tools that will be developed in the project.

Impact on People. Four highly skilled PDRAs with expertise in the cutting edge techniques for process planning under uncertainty will come out from this project and will be ready to take leading roles in industry, academia and governmental and financial organisations.

Impact on Knowledge. This project will push the boundaries of the state-of-the-art in the uncertainty modelling by categorising the uncertainty into different classes and incorporating it in making planning decisions. A hybrid modelling strategy, software prototype and different types of optimal policy decisions will expand the current knowledge base of process planning techniques.


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Charitopoulos V (2019) Closed-loop integration of planning, scheduling and multi-parametric nonlinear control in Computers & Chemical Engineering

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Cardoso-Silva J (2019) Optimal Piecewise Linear Regression Algorithm for QSAR Modelling. in Molecular informatics

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Charitopoulos V (2020) A game-theoretic optimisation approach to fair customer allocation in oligopolies in Optimization and Engineering

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Medina-González S (2020) A graph theory approach for scenario aggregation for stochastic optimisation in Computers & Chemical Engineering

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Medina-González S (2021) A reformulation strategy for mixed-integer linear bi-level programming problems in Computers & Chemical Engineering

Description New algorithms for multiparametric programming using symbolic solution
Scenario reduction for stochastic programming approaches
Integration of prodcution planning, scheduling and control for process industry
Exploitation Route potential use for applications for optimal decision making under uncertainty, multi-level modelling for process industry
Sectors Chemicals,Energy,Manufacturing, including Industrial Biotechology

Description Collaboration projects have been used to test ideas to industry.
First Year Of Impact 2020
Sector Chemicals,Energy,Manufacturing, including Industrial Biotechology
Impact Types Economic

Description Praxair/Linde supply chains 
Organisation Praxair Surface Technologies
Country United States 
Sector Private 
PI Contribution Collaborating Organisation and Project Partner
Collaborator Contribution Case studies for industrial gas supply chains
Impact journal publicatios
Start Year 2017