A multi-phase approach to university course timetabling

ix, 117 leaves ; 29 cmCourse timetabling is a well known constraint satisfaction optimization (CSOP) problem, which needs to be solved in educational institutions regularly. Unfortunately, this course timetabling problem is known to be NP-complete [7, 39]. This M.Sc. thesis presents a multi-phase approach to solve the university level course timetabling problem. We decompose the problem into several sub-problems with reduced complexity, which are solved in separate phases. In phase-1a we assign lectures to professors, phase-1b assigns labs and tutorials to academic assistances and graduate assistants. Phase-2 assigns each lecture to one of the two day-sequences (Monday-Wednesday-Friday or Tuesday-Thursday). In Phase-3, lectures of each single day-sequence are then assigned to time-slots. Finally, in phase-4, labs and tutorials are assigned to days and time-slots. This decomposition allows the use of different techniques as appropriate to solve different phases. Currently different phases are solved using constraint programming and integer linear programming. The multi-phase architecture with the graphical user interface allows users to customize constraints as well as to generate new solutions that may incorporate partial solutions from previously generated feasible solutions

Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem

We consider the university course timetabling problem, which is one of the most studied problems in educational timetabling. In particular, we focus our attention on the formulation known as the curriculum-based course timetabling problem, which has been tackled by many researchers and for which there are many available benchmarks. The contribution of this paper is twofold. First, we propose an effective and robust single-stage simulated annealing method for solving the problem. Secondly, we design and apply an extensive and statistically-principled methodology for the parameter tuning procedure. The outcome of this analysis is a methodology for modeling the relationship between search method parameters and instance features that allows us to set the parameters for unseen instances on the basis of a simple inspection of the instance itself. Using this methodology, our algorithm, despite its apparent simplicity, has been able to achieve high quality results on a set of popular benchmarks. A final contribution of the paper is a novel set of real-world instances, which could be used as a benchmark for future comparison

A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

Copyright @ Springer Science + Business Media. All rights reserved.The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02

Design, Engineering, and Experimental Analysis of a Simulated Annealing Approach to the Post-Enrolment Course Timetabling Problem

The post-enrolment course timetabling (PE-CTT) is one of the most studied timetabling problems, for which many instances and results are available. In this work we design a metaheuristic approach based on Simulated Annealing to solve the PE-CTT. We consider all the different variants of the problem that have been proposed in the literature and we perform a comprehensive experimental analysis on all the public instances available. The outcome is that our solver, properly engineered and tuned, performs very well on all cases, providing the new best known results on many instances and state-of-the-art values for the others

Automatically detecting neighbourhood constraint interactions using comet

The major benet of using events as the basis for our detection system is the clean separation between the neighbourhoods and detector which we can achieve. The detector simply iterates over a set of Neighbourhood objects and checks each for interactions. The acceptance function for the neighbourhood is set to accept any tness. For purposes of detecting an interaction it does not matter whether a move reduces or increases the constraint violations; both indicate that a relationship exists. The simulation is performed in two stages. Starting from a randomly created initial solution a random move from the neighbourhood is chosen, often this will lead to a constraint change and prevent the need for further exploration. For some constraints the chance of randomly selecting a move which would violate it is fairly low and so a more rigourous search is required. If the initial move has not found any interaction then the detector explores every neighbouring state from the current position. If at any stage a change of the constraint violations is detected then the exploration is stopped

Automatically detecting neighbourhood constraint interactions using Comet

Local Search has been shown to be capable of producing high quality solutions in a variety of hard constraint and optimisation problems. Typically implementing a Local Search algorithm is done in a problem specic manner. In the last few years a variety of approaches have emerged focussed on easing the implementation and creating a clean separation between the algorithm and problem. We present a system which can deduce information about the interactions between problem constraints and the search neighbourhoods whilst maintaining a loose coupling between these components. We apply this technique to the International Timetabling Competition instances and show an implementation expressed in Comet

Operational Research in Education

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

Cyclic transfers in school timetabling

In this paper we propose a neighbourhood structure based on sequential/cyclic moves and a cyclic transfer algorithm for the high school timetabling problem. This method enables execution of complex moves for improving an existing solution, while dealing with the challenge of exploring the neighbourhood efficiently. An improvement graph is used in which certain negative cycles correspond to the neighbours; these cycles are explored using a recursive method. We address the problem of applying large neighbourhood structure methods on problems where the cost function is not exactly the sum of independent cost functions, as it is in the set partitioning problem. For computational experiments we use four real world data sets for high school timetabling in the Netherlands and England.We present results of the cyclic transfer algorithm with different settings on these data sets. The costs decrease by 8–28% if we use the cyclic transfers for local optimization compared to our initial solutions. The quality of the best initial solutions are comparable to the solutions found in practice by timetablers