Centralized and standardized molecular testing for genetic alterations associated with a high risk of malignancy efficiently complements the local cytopathologic diagnosis of thyroid nodule aspirates in the clinical setting. Actionable molecular cytology can improve the personalized surgical and medical management of patients with thyroid cancers, facilitating one-stage total thyroidectomy and reducing the number of unnecessary diagnostic surgeries.

Many optimisation problems are of an online—also called dynamic—nature, where new information is expected to arrive and the problem must be resolved in an ongoing fashion to (a) improve or revise previous decisions and (b) take new ones. Typically, building an online decision-making system requires substantial ad-hoc coding to ensure the offline version of the optimisation problem is continually adjusted and resolved. This paper defines a general framework for automatically solving online optimisation problems. This is achieved by extending a model of the offline optimisation problem, from which an online version is automatically constructed, thus requiring no further modelling effort. In doing so, it formalises many of the aspects that arise in online optimisation problems. The same framework can be applied for automatically creating sliding-window solving approaches for problems that have a large time horizon. Experiments show we can automatically create efficient online and sliding-window solutions to optimisation problems.

Online optimization approaches are popular for solving optimization problems where not all data is considered at once, because it is computationally prohibitive, or because new data arrives in an ongoing fashion. Online approaches solve the problem iteratively, with the amount of data growing in each iteration. Over time, many problem variables progressively become realized, i.e., their values were fixed in the past iterations and they can no longer affect the solution. If the solving approach does not remove these realized variables and associated data and simplify the corresponding constraints, solving performance will slow down significantly over time. Unfortunately, simply removing realized variables can be incorrect, as they might affect unrealized decisions. This is why this complex task is currently performed manually in a problemspecific and time-consuming way. We propose a problem-independent framework to identify realized data and decisions, and remove them by summarizing their effect on future iterations in a compact way. The result is a substantially improved model performance.