No abstract

This paper presents HyFlex, a software framework for the development of cross-domain search methodologies. The framework features a common software interface for dealing with different combinatorial optimisation problems and provides the algorithm components that are problem specific. In this way, the algorithm designer does not require a detailed knowledge of the problem domains and thus can concentrate his/her efforts on designing adaptive general-purpose optimisation algorithms. Six hard combinatorial problems are fully implemented: maximum satisfiability, one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing. Each domain contains a varied set of instances, including real-world industrial data and an extensive set of state-of-the-art problem specific heuristics and search operators. HyFlex represents a valuable new benchmark of heuristic search generality, with which adaptive cross-domain algorithms are being easily developed and reliably compared.This article serves both as a tutorial and a as survey of the research achievements and publications so far using HyFlex.

The 2nd International Timetabling Competition (ITC2007) was announced on the 1st August 2007. Building on the success of the first, this competition aimed to further develop interest in the area of educational timetabling while providing researchers with models of the problems faced which incorporate an increased number of real world constraints. A main objective of the competition was that conclusions drawn would further stimulate debate within the widening timetabling research community. The overall aim of the competition was to create better understanding between researchers and practitioners by allowing emerging techniques to be trialed and tested on real world models of timetabling problems. The competition was divided into three tracks to reflect the important variations which exist within educational timetabling within Higher Education. As these formulations incroporate an increased number of 'real world' issues, it is anticipated that the competition will set the research agenda within the field. After finishing on the 25th January 2008, final results of the competition are to be made available in May 2008. Along with background to the competition, the tracks are described here together with initial results for the datasets released.

Single-player games (often called puzzles) have received considerable attention from the scientific community. Consequently, interesting insights into some puzzles, and into the approaches for solving them, have emerged. However, many puzzles have been neglected, possibly because they are unknown to many people. In this article, we survey NP-Complete puzzles in the hope of motivating further research in this fascinating area, particularly for those puzzles which have received little scientific attention to date.

Online bin-packing is a well-known problem in which immediate decisions must be made about the placement of items with various sizes into fixed capacity bins. The associated decisions can be based on an index policy in which each decision option is independently given a value and the highest value choice is selected. In this paper, we represent such heuristics for online bin packing as a simple matrix of scores. We then use a genetic algorithm to search for matrices giving good performance. This might be regarded as parameter tuning of the packing heuristic but in which a fine-grained representation is used and so the number of parameters is much larger than in standard parameter tuning. The evolved matrices perform better than the standard heuristics. They also reveal interesting structures and so have impact on questions of how heuristic score functions should be represented and what structure they might be expected to exhibit.