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.

We propose a constraint programming approach to the popular matching problem. We show that one can use the Global Cardinality Constraint to encode the problem even in cases that involve ties in the ordinal preferences of the applicants.

Different formulations are proposed, distinguishing The notion of popular matching was introduced by (Gardenfors between one-sided matching (Garg et al. 2010) and twosided 1975), but this notion has its roots in the 18th century matching, e.g. the stable marriage (SM) problem (Gale and the notion of a Condorcet winner.

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.

The last decade has seen a growing interest in solver portfolios, automated solver configuration, and runtime prediction methods. At their core, these methods rely on a deterministic, consistent behaviour from the underlying algorithms and solvers. However, modern state-of-the-art solvers have elementsof stochasticity built in such as randomised variable and value selection, tie-breaking, and randomised restarting. Such features can elicit dramatic variations in the overall performance between repeated runs of the solver,often by several orders of magnitude. Despite the success of the aforementioned fields, such performance variations in the underlying solvers have largely been ignored. Supported by a large-scale empirical study employing many years of industrial SAT Competition instances including repeated runs, we present statistical and empirical evidence that such a performance variation phenomenon necessitates a change in the evaluation of portfolio, runtime prediction, and automated configuration methods. In addition, we demonstrate that this phenomenon can have a significant impact on empirical solver competitions. Specifically, we show that the top three solvers from the 2014 SAT Competition could have been ranked in any permutation. These findings demonstrate the need for more statistically well-founded regimes in empirical evaluations.

This paper outlines an online approach for algorithm configuration which uses the power of modern multicore system to evaluate multiple parameters configurations in parallel.

We describe a constraint-based timetabling system that was developed for the dental school based at Cork University Hospital in Ireland. This sy stem has been deployed since 2010. Dental school timetabling differs from other university course scheduling in that certain clinic sessions can be used by multiple courses at the same time, provided a limit on room capacity is satisfied. Starting from a constraint programming solution using a web interface, we have moved to a mixed integer programming-based solver to deal with multiple objective functions, along with a dedicated Java application, which provides a rich user interface. Solutions for the years 2010, 2011 and 2012 have been used in the dental school, replacing a manual timetabling process, which could no longer cope with increasing student numbers and resulting resource bottlenecks. The use of the automated system allowed the dental school to increase the number of students enrolled to the maximum possible given the available resources. It also provides the school with a valuable “what-if” analysis tool.

Benjamin Grosof (Coherent Knowledge from episodic memory to great progress is being made on methods Systems) on representing activity create semantic memory, using a combination to solve problems related to structure context through semantic rule methods, of semantic memory and prediction, motion simulation, deriving from experience in the episodic memory to guide users?

We describe a constraint-based timetabling system that was developed for the dental school based at Cork University Hospital in Ireland.This system has been deployed since 2010.Dental school timetabling differs from other university course scheduling in that certain clinic sessions can be used by multiple courses at the same time, provided a limit on room capacity is satisfied.Starting from a constraint programming solution using a web interface, we have moved to a mixed integer programming-based solver to deal with multiple objective functions, along with a dedicated Java application, which provides a rich user interface.Solutions for the years 2010, 2011 and 2012 have been used in the dental school, replacing a manual timetabling process, which could no longer cope with increasing student numbers and resulting resource bottlenecks.The use of the automated system allowed the dental school to increase student numbers to the maximum possible given the available resources.It also provides the school with a valuable "what-if" analysis tool.

Constraint programming has become an important technology for solving hard combinatorial problems in a diverse range of application domains. It has its roots in artificial intelligence, mathematical programming, op- erations research, and programming languages. This paper gives a perspective on where constraint programming is today, and discusses a number of opportunities and challenges that could provide focus for the research community into the future.