Algorithm selection is of increasing practical relevance in a variety of applications. The interested reader is referred to a recent survey for more information (Kotthoff 2014). All submissions were required to provide the full source code, with instructions on how to run the system. In alphabetical order, the submitted systems were ASAP kNN, ASAP RF, autofolio, flexfolio-schedules, sunny, sunny-presolv, zilla, and zillafolio. The overall winner of the ICON challenge was zilla, based on the prominent SATzilla (Xu et al. 2008) system.

Boliden Tara Mines Ltd. consumed 184.7 GWh of electricity in 2014, equating to over 1% of the national demand of Ireland or approximately 35,000 homes. Ireland’s industrial electricity prices, at an average of 13 c/KWh in 2014, are amongst the most expensive in Europe. Cost effective electricity procurement is ever more pressing for businesses to remain competitive. In parallel, the proliferation of intelligent devices has led to the industrial Internet of Things paradigm becoming mainstream. As more and more devices become equipped with network connectivity, smart metering is fast becoming a means of giving energy users access to a rich array of consumption data. These modern sensor networks have facilitated the development of applications to process, analyse, and react to continuous data streams in real-time. Subsequently, future procurement and consumption decisions can be informed by a highly detailed evaluation of energy usage. With these considerations in mind, this paper uses variable energy prices from Ireland’s Single Electricity Market, along with smart meter sensor data, to simulate the scheduling of an industrial-sized underground pump station in Tara Mines. The objective is to reduce the overall energy costs whilst still functioning within the system’s operational constraints. An evaluation using real-world electricity prices and detailed sensor data for 2014 demonstrates significant savings of up to 10.72% over the year compared to the existing control systems.

We show how both techniques can be combined MaxSAT is the optimization version of the Satisfiability and empirically demonstrate on SAT that our improved (SAT) problem. It can be used effectively to model problems method works notably better than the original method and in several domains, such as scheduling, timetabling, other instance-specific algorithm tuners. We then apply the FPGA routing, design and circuit debugging, software package new technique to MaxSAT. Finally, in extensive experiments installation, bioinformatics, probabilistic reasoning, etc. we show that the developed solvers significantly outperform From the research perspective, MaxSAT is also of particular the current state-of-the-art in every MaxSAT domain.