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.

Abstract. There is a perception that teaching space in universities is a rather scarce resource. However, some studies have revealed that in many institutions it is actually chronically under-used. Often, rooms are occupied only half the time, and even when in use they are often only half full. This is usually measured by the "utilisation" which is defined as the percentage of available 'seat-hours' that are employed. Within real institutions, studies have shown that this utilisation can often take values as low as 20-40%.One consequence of such a low level of utilisation is that space managers are under pressure to make a more efficient use of the available teaching space. However, better management is hampered because there does not appear to be a good understanding within space management (nearterm planning) of why this happens. Nor, a good basis within space planning (long-term planning) of how best to accommodate the expected low utilisations. This motivates our two main goals: (i) To understand the factors that drive down utilisations, (ii) To set up methods to provide better space planning.Here, we provide quantitative evidence that constraints arising from timetabling and location requirements easily have the potential to explain the low utilisations seen in reality. Furthermore, on considering the decision question "Can this given set of courses all be allocated in the available teaching space?" we find that the answer depends on the associated utilisation in a way that exhibits threshold behaviour: There is Contact Author (Authors listed alphabetically.) 2 Towards Improving Utilisation a sharp division between regions in which the answer is "almost always yes" and those of "almost always no". Through analysis and understanding of the space of potential solutions, our work suggests that better use of space within universities will come about through an understanding of the effects of timetabling constraints and when it is statistically likely that it will be possible for a set of courses to be allocated to a particular space. The results presented here provide a firm foundation for university managers to take decisions on how space should be managed and planned for more effectively. Our multi-criteria approach and new methodology together provide new insight into the the interaction between the course timetabling problem and the crucial issue of space planning.

Automated examination timetabling has been addressed by a wide variety of methodologies and techniques over the last ten years or so. Many of the methods in this broad range of approaches have been evaluated on a collection of benchmark instances provided at the University of Toronto in 1996. Whilst the existence of these datasets has provided an invaluable resource for research into examination timetabling, the instances have significant limitations in terms of their relevance to real-world examination timetabling in modern universities. This paper presents a detailed model which draws upon experiences of implementing examination timetabling systems in universities in Europe, Australasia and America. This model represents the problem that was presented in the 2nd International Timetabling Competition (ITC2007). In presenting this detailed new model, this paper describes the examination timetabling track introduced as part of the competition. In addition to the model, the datasets used in the competition are also based on current real-world instances introduced by EventMAP Limited. It is hoped that the interest generated as part of the competition will lead to the development, investigation and application of a host of novel and exciting techniques to address this important real-world search domain. Moreover, the motivating goal of this paper is to close the currently existing gap between theory and practice in examination timetabling by presenting the research community with a rigorous model which represents the complexity of the real-world situation. In this paper we describe the model and its motivations, followed by a full formal definition

Early childhood inorganic arsenic (i-As) exposure is of particular concern since it may adversely impact on lifetime health outcomes. Infants’ urinary arsenic (As) metabolites were analysed in 79 infants by inductively coupled plasma—mass spectrometric detection (IC-ICP-MS) to evaluate i-As exposure pre- and post-weaning. Levels of i-As in rice-based weaning and infants’ foods were also determined to relate to urinary As levels. Higher As levels, especially of monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA), were found in urine from formula fed infants compared to those breastfed. Urine from infants post-weaning consuming rice-products resulted in higher urinary MMA and DMA compared to the paired pre-weaning urine samples. The European Union (EU) has regulated i-As in rice since 1st January 2016. Comparing infants’ rice-based foods before and after this date, little change was found. Nearly ¾ of the rice-based products specifically marketed for infants and young children contained i-As over the 0.1 mg/kg EU limit. Efforts should be made to provide low i-As rice and rice-based products consumed by infants and young children that do not exceed the maximum i-As level to protect this vulnerable subpopulation.

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This paper describes the development of a novel metaheuristic that combines an electromagnetic-like mechanism (EM) and the great deluge algorithm (GD) for the University course timetabling problem. This well-known timetabling problem assigns lectures to specific numbers of timeslots and rooms maximizing the overall quality of the timetable while taking various constraints into account. EM is a population-based stochastic global optimization algorithm that is based on the theory of physics, simulating attraction and repulsion of sample points in moving toward optimality. GD is a local search procedure that allows worse solutions to be accepted based on some given upper boundary or 'level'. In this paper, the dynamic force calculated from the attraction-repulsion mechanism is used as a decreasing rate to update the 'level' within the search process. The proposed method has been applied to a range of benchmark university course timetabling test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature, demonstrating that the method is able to produce improved solutions to those currently published. We believe this is due to the combination of both approaches and the ability of the resultant algorithm to converge all solutions at every search process.

Abstract. Course Scheduling consists of assigning lecture events to a limited set of specific timeslots and rooms. The objective is to satisfy as many soft constraints as possible, while maintaining a feasible solution timetable. The most successful techniques to date require a compute-intensive examination of the solution neighbourhood to direct searches to an optimum solution. Although they may require fewer neighbourhood moves than more exhaustive techniques to gain comparable results, they can take considerably longer to achieve success. This paper introduces an extended version of the Great Deluge Algorithm for the Course Timetabling problem which, while avoiding the problem of getting trapped in local optima, uses simple Neighbourhood search heuristics to obtain solutions in a relatively short amount of time. The paper presents results based on a standard set of benchmark datasets, beating over half of the currently published best results with in some cases up to 60% of an improvement.

Software refactoring has been recognised as a valuable process during software development and is often aimed at repaying technical debt. Technical debt arises when a software product has been built or amended without full care for structure and extensibility. Refactoring is useful to keep technical debt low and if it can be automated there are obvious efficiency benefits. Using a combination of automated refactoring techniques, software metrics and metaheuristic searches, an automated refactoring tool can improve the structure of a software system without affecting its functionality. In this paper, four different refactoring approaches are compared using an automated software refactoring tool. Weighted sums of metrics are used to form different fitness functions that drive the search process towards certain aspects of software quality. Metrics are combined to measure coupling, abstraction and inheritance and a fourth fitness function is proposed to measure reduction in technical debt. The 4 functions are compared against each other using 3 different searches on 6 different open source programs. Four out of the 6 programs show a larger improvement in the technical debt function after the search based refactoring process. The results show that the technical debt function is useful for assessing improvement in quality.