ADOPT

ADOPT - Advancing optimisation technologies through international collaboration

Duration:          2022 – 2026  

Funding:      Engineering and Physical Science Research Council

Project partners: 

JARA Center for Simulation and Data Science (RWTH Aachen, Jülich Research Center)

The Sargent Centre for Process Systems Engineering (Imperial College London, University College London)

Gurobi Optimization, LLC

MOSEK ApS

Octeract

Siemens

Project description:

The complex, interconnected and fast-changing nature of today's society presents a growing challenge for decision-makers.  Increased competition in the process industries (oil and gas, chemicals, personal care products, food, pharmaceuticals and agrochemicals) means that agility must be built into process design and operation.  Furthermore, the need to ensure reliability across the supply chain, minimise resource use and environmental impact, and maximise energy efficiency combine to make investment and operational decisions especially difficult.  Such multifaceted decision-making has long been aided by detailed mathematical models of physical and engineered processes, which enable digital twins and constitute a cornerstone of smart manufacturing technologies and the future Industry 4.0. But the full benefits afforded by these models have so far been hampered by the lack of tools for exploiting them beyond "what if?" scenario analysis. In particular, the uptake of optimisation-based decision-making has been hindered by the large-scale, nonlinear and uncertain nature of these problems that often leads to suboptimal or even unphysical solutions.

In the ADOPT collaboration between the Sargent Centre for Process Systems Engineering (CPSE) and the JARA Center for Simulation and Data Science (JARA-CSD), we propose to address some of these shortcomings by developing improved methods for deterministic global optimisation, a class of optimisation methods that rely on complete search techniques and offer a rigorous conceptual framework to overcome the caveats of local optimisation. Our key research hypothesis is that the integration of deterministic global optimisation with surrogate (simplified) models and machine learning will enable transformational changes in our capability to tackle complex decision-making problems, leading to more tractable solutions with global optimality certificates and improved resilience to uncertainty. This nascent area brings about the following specific research challenges that we shall tackle within ADOPT:

- identifying best-in-class theoretical / algorithmic global optimisation frameworks and surrogate modelling paradigms to empower surrogate-based optimisation;

- handling uncertainty within the chain linking physical/simulated data to surrogate models and to optimisation results; and

- developing bespoke deterministic global optimisation approaches for more challenging classes of problems beyond mixed-integer nonlinear programming.

The ADOPT collaboration brings together two world-class teams of researchers in the field of deterministic global optimisation as well as team members who are specialists in handling uncertainty, in solving large-scale combinatorial problems, and in applying optimisation to real-world engineering problems. Furthermore, our assembled team partners with prominent optimisation software and process modelling companies in order to increase the accessibility of the research outputs and facilitate their dissemination.

The ADOPT collaboration creates added-value through the combined strength of scientific expertise of the two centres, the breadth of the software infrastructure that can be brought together, the wealth of its human capital, the reach of its industrial relationships and the exceptional potential to establish a long-term partnership. It will lead to scientific advances that can be tested on practical problems quickly, ensuring maximum impact from the research.