Uncertainty-Aware Planning and Scheduling in the Process Industries
Lead Research Organisation:
University College London
Department Name: Chemical Engineering
Abstract
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.
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.
Publications
Aguirre A
(2017)
Mixed Integer Linear Programming Based Approaches for Medium-Term Planning and Scheduling in Multiproduct Multistage Continuous Plants
in Industrial & Engineering Chemistry Research
Aguirre A
(2018)
Optimisation approaches for supply chain planning and scheduling under demand uncertainty
in Chemical Engineering Research and Design
Aguirre A
(2017)
27th European Symposium on Computer Aided Process Engineering
Aguirre A
(2018)
Medium-term optimization-based approach for the integration of production planning, scheduling and maintenance
in Computers & Chemical Engineering
Burnak B
(2018)
Simultaneous Process Scheduling and Control: A Multiparametric Programming-Based Approach
in Industrial & Engineering Chemistry Research
Cardoso-Silva J
(2019)
Optimal Piecewise Linear Regression Algorithm for QSAR Modelling.
in Molecular informatics
Charitopoulos V
(2017)
A unified framework for model-based multi-objective linear process and energy optimisation under uncertainty
in Applied Energy
Charitopoulos V
(2020)
A game-theoretic optimisation approach to fair customer allocation in oligopolies
in Optimization and Engineering
Charitopoulos V
(2017)
27th European Symposium on Computer Aided Process Engineering
Charitopoulos V
(2019)
Closed-loop integration of planning, scheduling and multi-parametric nonlinear control
in Computers & Chemical Engineering
Charitopoulos V
(2018)
13th International Symposium on Process Systems Engineering (PSE 2018)
Charitopoulos V
(2016)
Explicit model predictive control of hybrid systems and multiparametric mixed integer polynomial programming
in AIChE Journal
Charitopoulos V
(2018)
Multi-parametric mixed integer linear programming under global uncertainty
in Computers & Chemical Engineering
Charitopoulos V
(2017)
Multi-parametric linear programming under global uncertainty
in AIChE Journal
Charitopoulos V
(2017)
Traveling Salesman Problem-Based Integration of Planning, Scheduling, and Optimal Control for Continuous Processes
in Industrial & Engineering Chemistry Research
Charitopoulos V
(2017)
Nonlinear Model-Based Process Operation under Uncertainty Using Exact Parametric Programming
in Engineering
Diangelakis N
(2017)
Process design and control optimization: A simultaneous approach by multi-parametric programming
in AIChE Journal
Hanasusanto G
(2016)
K -adaptability in two-stage distributionally robust binary programming
in Operations Research Letters
Hanasusanto G
(2015)
A comment on "computational complexity of stochastic programming problems"
in Mathematical Programming
Liu S
(2018)
Multi-objective optimisation for biopharmaceutical manufacturing under uncertainty
in Computers & Chemical Engineering
Liu S
(2016)
Integrated Optimization of Upstream and Downstream Processing in Biopharmaceutical Manufacturing under Uncertainty: A Chance Constrained Programming Approach
in Industrial & Engineering Chemistry Research
Liu S
(2018)
Fair profit distribution in multi-echelon supply chains via transfer prices
in Omega
Liu S
(2019)
Optimal Antibody Purification Strategies Using Data-Driven Models
in Engineering
Medina-González S
(2020)
A graph theory approach for scenario aggregation for stochastic optimisation
in Computers & Chemical Engineering
Medina-González S
(2021)
A reformulation strategy for mixed-integer linear bi-level programming problems
in Computers & Chemical Engineering
Medina-González S
(2019)
29th European Symposium on Computer Aided Process Engineering
Oberdieck R
(2017)
Explicit model predictive control: A connected-graph approach
in Automatica
Oberdieck R
(2016)
On unbounded and binary parameters in multi-parametric programming: applications to mixed-integer bilevel optimization and duality theory
in Journal of Global Optimization
Oberdieck R
(2016)
Multi-objective optimization with convex quadratic cost functions: A multi-parametric programming approach
in Computers & Chemical Engineering
Oberdieck R
(2016)
POP - Parametric Optimization Toolbox
in Industrial & Engineering Chemistry Research
Pistikopoulos E
(2016)
Towards the integration of process design, control and scheduling: Are we getting closer?
in Computers & Chemical Engineering
Silvente J
(2019)
Scenario tree reduction for optimisation under uncertainty using sensitivity analysis
in Computers & Chemical Engineering
Silvente J
(2017)
27th European Symposium on Computer Aided Process Engineering
Silvente J
(2017)
An MILP formulation for the optimal management of microgrids with task interruptions
in Applied Energy
Silvente J
(2018)
A rolling horizon approach for optimal management of microgrids under stochastic uncertainty
in Chemical Engineering Research and Design
Triantafyllidis CP
(2018)
An integrated platform for intuitive mathematical programming modeling using LaTeX.
in PeerJ. Computer science
| 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 |