Modelling and Solving Online Optimisation Problems

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.

Authors

• Alexander Ek
• Maria Garcia de la Banda Department of Data Science & AI (DSAI), Monash University, Australia ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA), Australia
• Andreas Schutt
• Peter J. Stuckey Monash University
• Guido Tack Monash University

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