Models, solution methods and threshold behaviour for the teaching space allocation problem

Universities have to manage their teaching space, and plan future needs. Their efforts are frequently hampered by, capital and maintenance costs, on one hand, pedagogical and teaching services on the other. The efficiency of space usage, can be measured by the utilisation: the percentage of available seat-hours actually used. The observed utilisation, in many institutions, is unacceptably low, and this provides our main underlying motivation: To address and assess some of the major factors that affect teaching space usage in the hope of improving it in practise. Also, when performing space management, managers operate within a limited number and capacity of lecture theatres, tutorial rooms, etc. Hence, some teaching activities require splitting into different groups. For example, lectures being too large to fit in any one room and seminars/tutorials being taught in small groups for good teaching practise. This thesis forms the cornerstone of ongoing research to illuminate issues stemming from poorly utilised space and studies the nature of constraints that underlies those low levels of utilisation. We give quantitative evidence that constraints related to timetabling are major players in pushing down utilisation levels and also, devise "Dynamic Splitting" algorithms to illustrate the effects of splitting on utilisation levels. We showed the existence of threshold between phases where splitting and allocation is "always possible" to ones where "it's never possible", hence, introducing a practical application of Phase Transition to space planning and management. We have also worked on the long-term planning aspect of teaching space and proposed methods to improve the future expected utilisation

Heuristic approaches for real world examination timetabling problems

The examination timetabling (exam-timeslot-room assignment) problem involves assigning exams to a specific or limited number of timeslots and rooms, with the aim of satisfying the hard constraints and the soft constraints as much as possible. Most of the techniques reported in the literature have been applied to solve simplified examination benchmark datasets, available within the scientific literature. In this research we bridge the gap between research and practice by investigating a problem taken from the Universiti Malaysia Pahang (UMP), a real world capacitated examination timetabling problem. This dataset has several novel constraints, in addition to those commonly used in the literature. Additionally, the invigilator scheduling problem (invigilator assignment) was also investigated as it has not received the same level of research attention as the examination scheduling (although it is just as important to educational institutions). The formal models are defined, and constructive heuristics was developed for both problems in which the overall problems are solved with a two-phase approach which involves scheduling the exam to timeslot and room, and follows with scheduling the invigilator. During the invigilator assignment, we assume that there is already an examination timetable in place (i.e. previously generated). It reveals that the invigilator scheduling solution dependent on the number of rooms selected from the exam-timeslot-room assignment phase (i.e. a lesser number of used rooms would minimises the invigilation duties for staff), this encourages us to further improve the exam-timeslot-room timetable solution. An improvement on the result was carried out using modified extended great deluge algorithm (modified-GDA) and multi-neighbourhood GDA approach (that use more than one neighbourhood during the search). The modified-GDA uses a simple to understand parameter and allows the boundary that acts as the acceptance level, to dynamically change during the search. The propose approaches able to produce good quality solution when compared to the solutions from the proprietary software used by UMP. In addition, our solutions adhere to all hard constraints which the current systems fail to do. Finally, we extend our research onto investigating the Second International Timetabling Competition (ITC2007) dataset as it also contains numerous constraints much similar to UMP datasets. Our propose approach able to produce competitive solutions when compared to the solutions produced by other reported works in the literature

Heuristic approaches for real world examination timetabling problems

The examination timetabling (exam-timeslot-room assignment) problem involves assigning exams to a specific or limited number of timeslots and rooms, with the aim of satisfying the hard constraints and the soft constraints as much as possible. Most of the techniques reported in the literature have been applied to solve simplified examination benchmark datasets, available within the scientific literature. In this research we bridge the gap between research and practice by investigating a problem taken from the Universiti Malaysia Pahang (UMP), a real world capacitated examination timetabling problem. This dataset has several novel constraints, in addition to those commonly used in the literature. Additionally, the invigilator scheduling problem (invigilator assignment) was also investigated as it has not received the same level of research attention as the examination scheduling (although it is just as important to educational institutions). The formal models are defined, and constructive heuristics was developed for both problems in which the overall problems are solved with a two-phase approach which involves scheduling the exam to timeslot and room, and follows with scheduling the invigilator. During the invigilator assignment, we assume that there is already an examination timetable in place (i.e. previously generated). It reveals that the invigilator scheduling solution dependent on the number of rooms selected from the exam-timeslot-room assignment phase (i.e. a lesser number of used rooms would minimises the invigilation duties for staff), this encourages us to further improve the exam-timeslot-room timetable solution. An improvement on the result was carried out using modified extended great deluge algorithm (modified-GDA) and multi-neighbourhood GDA approach (that use more than one neighbourhood during the search). The modified-GDA uses a simple to understand parameter and allows the boundary that acts as the acceptance level, to dynamically change during the search. The propose approaches able to produce good quality solution when compared to the solutions from the proprietary software used by UMP. In addition, our solutions adhere to all hard constraints which the current systems fail to do. Finally, we extend our research onto investigating the Second International Timetabling Competition (ITC2007) dataset as it also contains numerous constraints much similar to UMP datasets. Our propose approach able to produce competitive solutions when compared to the solutions produced by other reported works in the literature

Fuzzy methodologies for automated University timetabling solution construction and evaluation

This thesis presents an investigation into the use of fuzzy methodologies for University timetabling problems. The first area of investigation is the use of fuzzy techniques to combine multiple heuristic orderings within the construction of timetables. Different combinations of multiple heuristic ordering were examined, considering five graph-based heuristic orderings - Largest Degree, Saturation Degree, Largest Enrolment, Largest Coloured Degree and Weighted Largest Degree. The initial development utilised only two heuristic orderings simultaneously and subsequent development went on to incorporate three heuristic orderings simultaneously. A central hypothesis of this thesis is that this approach provides a more realistic scheme for measuring the difficulty of assigning events to time slots than the use of a single heuristic alone. Experimental results demonstrated that the fuzzy multiple heuristic orderings (with parameter tuning) outperformed all of the single heuristic orderings and non-fuzzy linear weighting factors. Comprehensive analysis has provided some key insights regarding the implementation of multiple heuristic orderings. Producing examination timetables automatically has been the subject of much research. It is generally the case that a number of alternative solutions that satisfy all the hard criteria are possible. Indeed, there are usually a very large number of such feasible solutions. Some method is required to permit the overall quality of different solutions to be quantified, in order to allow them to be compared, so that the best may be selected. In response to that demand, the second area of investigation of this thesis is concerned with a new evaluation function for examination timetabling problems. A novel approach, in which fuzzy methods are used to evaluate the end solution quality, separate from the objective functions used in solution generation, represents a significant addition to the literature. The proposed fuzzy evaluation function provides a mechanism to allow an overall decision in evaluating the quality of a timetable solution to be made based on common sense rules that encapsulate the notion that the timetable solution quality increases as both the average penalty and the highest penalty decrease. New algorithms to calculate what is loosely termed the lower limits and upper limits of the proximity cost function for any problem instance are also presented. These limits may be used to provide a good indication of how good any timetable solution is. Furthermore, there may be an association between the proposed lower limit and the formal lower bound. This is the first time that lower limits (other than zero) have been established for proximity cost evaluation of timetable solutions

Livro de atas do XVI Congresso da Associação Portuguesa de Investigação Operacional

Fundação para a Ciência e Tecnologia - FC