Tuning Bayesian Optimization for Problems with Dynamic Resource Constraints

Closed-loop optimization deals with problems in which candidate solutions are evaluated by conducting experiments, e.g. physical or biochemical experiments. Although this form of optimization is becoming more popular across the sciences, it may be subject to rather unexplored resourcing issues, as any experiment may require resources in order to be conducted. In this PhD project we are concerned with understanding how Bayesian optimization is affected by dynamic resource constraints - a type of constraint that models the availability of resources (e.g. raw materials, storage, computing power and storage, skilled engineers, equipment and machines, budget, etc) required to conduct a physical experiment or run a time-consuming simulation - and the development of search strategies to tackle this particular problem issue.

We expect the project to make a number of contributions:
1 Expand on the literature of dynamic resource constraints with additional types of constraints and concrete industrial examples.
2 Investigate, empirically and theoretically, the effect of dynamic resource constraints on Bayesian optimization (as opposed to operations research methods as done largely in the literature so far).
3 Develop and analyze various search strategies augmented on a Bayesian optimizer for coping with dynamic resource constraints.
4 Address the issue of hyperparameter optimization for the methods developed.
5 Augment the methodology with explainable AI to provide end-users with an understanding of key features affecting resource usage and optimization performance, and potentially utilise information from the explanations for informing the optimization process.

The presence of noise, uncertainty, robustness and non-homogenous experimental costs will also be factored into the analysis as deemed appropriate. Inspiration for search strategies will be drawn from a number of research fields including Machine Learning, Statistics, and Operations Research.

As Bayesian optimisation is a Machine Learning technique, this directly aligns with the EPSRC's strategic focus on Artificial Intelligence technologies. Moreover, this aligns strongly with the EPSRC's C1 ambition for AI technologies, to "Enable a competitive, data-driven economy", as the work looks to develop smart tools to solve real-world dynamic industrial optimisation problems that can have significant impact on a broad variety of challenges across a range of industries.