Explainable Algorithm Selection and Configuration through Instance Space

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Picure of futuristic data

This research project aims to:

  • Improve the explainability of optimisation algorithms.
  • Develop tools to support the automatic configuration of the most suitable algorithms to solve many real-world problems arising in logistics, manufacturing, healthcare, and other fields.

The details

This project will develop new analysis methods to help explain the performance (or lack thereof) of the algorithms that are used daily to plan our deliveries, organise our manufacturing plants, schedule our bus routes, optimise our supply chains, etc.

By intelligently exploring the space of possible instances of a problem and analysing the behaviour of the algorithms for such instances, the techniques developed in this project will be able to explain to decision-makers under which conditions we can expect those algorithms to provide trustworthy solutions and when we may expect that the solutions provided will be infeasible or suboptimal.

Outcomes of the project include new tools for automated algorithm selection and configuration, evaluated on a series of real-world industrial optimisation problems.

The graduate researcher on this project is: Anthony Rasulo

Supervision team

Other joint PhD projects