Decomposition, Reformulation, and Diving in University Course Timetabling

In many real-life optimisation problems, there are multiple interacting components in a solution. For example, different components might specify assignments to different kinds of resource. Often, each component is associated with different sets of soft constraints, and so with different measures of soft constraint violation. The goal is then to minimise a linear combination of such measures. This paper studies an approach to such problems, which can be thought of as multiphase exploitation of multiple objective-/value-restricted submodels. In this approach, only one computationally difficult component of a problem and the associated subset of objectives is considered at first. This produces partial solutions, which define interesting neighbourhoods in the search space of the complete problem. Often, it is possible to pick the initial component so that variable aggregation can be performed at the first stage, and the neighbourhoods to be explored next are guaranteed to contain feasible solutions. Using integer programming, it is then easy to implement heuristics producing solutions with bounds on their quality. Our study is performed on a university course timetabling problem used in the 2007 International Timetabling Competition, also known as the Udine Course Timetabling Problem. In the proposed heuristic, an objective-restricted neighbourhood generator produces assignments of periods to events, with decreasing numbers of violations of two period-related soft constraints. Those are relaxed into assignments of events to days, which define neighbourhoods that are easier to search with respect to all four soft constraints. Integer programming formulations for all subproblems are given and evaluated using ILOG CPLEX 11. The wider applicability of this approach is analysed and discussed.Comment: 45 pages, 7 figures. Improved typesetting of figures and table

Curriculum architecture - a literature review

The analysis of almost 400 abstracts, articles, books from academic sources, policy documents and the educational press has been undertaken to attempt to illuminate the concept of Curriculum Architecture. The phrase itself is not current in the Scottish educational discourse. This review has attempted to look at the international research literature, available over the past ten years or so, on the sub-themes identified in the SEED specification

Post enrolment based course timetabling: a description of the problem model used for track two of the second International Timetabling Competition

In this paper we give a detailed description of the problem model used in track-two of the second International Timetabling Competition, 2007-2008 www.cs.qub.ac.uk/itc2007/). This model is an extension of that used in the first timetabling competition, and we discuss the rationales behind these extensions. We also describe in detail the criteria that are used for judging solution quality and discuss other issues that are related to this. Finally we go over some of the strengths and limitations of the model. This paper can be regarded as the official documentation for track-two of the timetabling competition

Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design

"You have to get wet to learn how to swim" applied to bridging the gap between research into personnel scheduling and its implementation in practice

Personnel scheduling problems have attracted research interests for several decades. They have been considerably changed over time, accommodating a variety of constraints related to legal and organisation requirements, part-time staff, flexible hours of staff, staff preferences, etc. This led to a myriad of approaches developed for solving personnel scheduling problems including optimisation, meta-heuristics, artificial intelligence, decision-support, and also hybrids of these approaches. However, this still does not imply that this research has a large impact on practice and that state-of-the art models and algorithms are widely in use in organisations. One can find a reasonably large number of software packages that aim to assist in personnel scheduling. A classification of this software based on its purpose will be proposed, accompanied with a discussion about the level of support that this software offers to schedulers. A general conclusion is that the available software, with some exceptions, does not benefit from the wealth of developed models and methods. The remaining of the paper will provide insights into some characteristics of real-world scheduling problems that, in the author’s opinion, have not been given a due attention in the personnel scheduling research community yet and which could contribute to the enhancement of the implementation of research results in practice. Concluding remarks are that in order to bridge the gap that still exists between research into personnel scheduling and practice, we need to engage more with schedulers in practice and also with software developers; one may say we need to get wet if we want to learn how to swim

What is Effective Feedback? A Comparison of Views of Students and Academics

In recent years the literature regarding feedback has flourished with many researchers exploring the various factors related to student feedback. Researchers have focussed on individual aspects of feedback, such as timing or quality, and mainly explored the views of students to develop ways to increase the student satisfaction. However, with relatively low scores still being achieved through the National Student Survey (NSS), the question ‘why are students unhappy with feedback?’ still remains. The purpose of this research was to explore the perception of academic staff and students regarding what is effective feedback? A total of 9 students and 10 academics participated for this study. Participants were randomly allocated to either one-to-one interviews or focus groups. All collected data was analysed to highlight common themes between academics and students. The data revealed many inconsistencies in the provision of feedback and highlighted that there is no single factor resulting in student dissatisfaction. Three specific action plans were developed to help address the many contributing factors to students’ dissatisfaction with feedback. These action plans will then aim to have a beneficial influence on the student experience and their learning process. Keywords: Feedback; Higher Education; Grounded Theory. DOI: 10.7176/JEP/12-30-04 Publication date:October 31st 202

Integer programming for minimal perturbation problems in university course timetabling

In this paper we present a general integer programming-based approach for the minimal perturbation problem in university course timetabling. This problem arises when an existing timetable contains hard constraint violations, or infeasibilities, which need to be resolved. The objective is to resolve these infeasibilities while minimising the disruption or perturbation to the remainder of the timetable. This situation commonly occurs in practical timetabling, for example when there are unexpected changes to course enrolments or available rooms. Our method attempts to resolve each infeasibility in the smallest neighbourhood possible, by utilising the exactness of integer programming. Operating within a neighbourhood of minimal size keeps the computations fast, and does not permit large movements of course events, which cause widespread disruption to timetable structure. We demonstrate the application of this method using examples based on real data from the University of Auckland

Educational timetabling: Problems, benchmarks, and state-of-the-art results

We propose a survey of the research contributions on the field of Educational Timetabling with a specific focus on “standard” formulations and the corresponding benchmark instances. We identify six of such formulations and we discuss their features, pointing out their relevance and usability. Other available formulations and datasets are also reviewed and briefly discussed. Subsequently, we report the main state-of-the-art results on the selected benchmarks, in terms of solution quality (upper and lower bounds), search techniques, running times, and other side settings