Decision-making under Uncertainty for COmplex systems (DUCO) team

Our vision: Unlock the true power of randomness and connection.

A system operates through complexly connected components, subjected to various randomness.
At the DUCO team, we believe that the complex randomness and connections within a system are keys to system resilience and efficiency.
Our mission is to explain, optimise, and harness these two keys, ensuring systems operate at their best, in the face of risks and uncertainties.

Our research

Matrix-based Bayesian network (MBN)

MBN and the Python toolkit MBNPy are a tool to enable Bayesian network (BN) to handle large-scale systems. BN is a useful tool to visualise complex probabilistic dependence between various types of variables (see the figure on the left-hand side).

MBN solves the memory issue arising from the converging structure between components and the system. This is done by its encoding scheme of the high-dimensional probability distribution $P(S | X_1,\cdots,X_N)$

With MBN, one can perform probabilistic analysis of complex AND large-scale system problems. See for example, the figure below.
Possible analyses include reliability assessment, component importance measure, Value of Information, Bayesian inference of inspection results, and uncertainty-based optimisation.

Buffered optimisation and reliability method (BORM)

Reliabiltiy-based optimisation (RBO) aims to find the most economical solution that satisfies the reliability requirement; see the figure below.

As a tool for RBO, BORM greatly improves computational efficiency, especially in data-driven settings (Byun and Royset 2022) and given a general, large-scale system (Byun et al. 2023). The large power system in the figure below has been solved by BORM within 1.5 hours with a personal laptop.

If interested, check out the Matlab codes at GitHub. We proudly say that it works quite well.