Data Driven Automated Scheduling under Correlated Uncertainty

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The project involves the design and development of data-driven automated scheduling under correlated uncertainty, and falls within the EPSRC Mathematical Sciences areas of Statistics and Applied Probability, Mathematical Analysis, and AI.Correlated uncertainty frequently arises in practice, such as routing under uncertain traffic, weather dependent scheduling, sensor placement and measurement of pollution, diffusion in social networks, among others.The problem attracts interests in methodological advances for data-driven approaches and automated planning and scheduling (APS). APS is a key technique in decision theory, in which a solution must optimise over a multidimensional space to synthesize a schedule or behaviour that is predicted to address some desired goals.Typical approaches in APS are either oblivious to data leading to conservative decisions, or make distributional assumptions that perform poorly out-of-sample. Data-driven approaches offer a middle ground that involve an estimation of uncertain input parameters based on partially available information. This can be coupled with correlations within the data to build scheduling models that enable robust decision-making without becoming too conservative.The supervisors have complementary experience in APS and Optimisation, and are perfectly suited to supervise this project.