Showing papers by "Rong Qu published in 2007"

Solving a practical examination timetabling problem: a case study

Abstract: This paper presents a Greedy-Least Saturation Degree (G-LSD) heuristic (which is an adaptation of the least saturation degree heuristic) to solve a real-world examination timetabling problem at the University Kebangsaan, Malaysia. We utilise a new objective function that was proposed in our previous work to evaluate the quality of the timetable. The objective function considers both timeslots and days in assigning exams to timeslots, where higher priority is given to minimise students having consecutive exams on the same day. The objective also tries to spread exams throughout the examination period. This heuristic has the potential to be used for the benchmark examination datasets (e.g. the Carter datasets) as well as other real world problems. Computational results are presented.

Discrete Optimization A graph-based hyper-heuristic for educational timetabling problems

Abstract: This paper presents an investigation of a simple generic hyper-heuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within the hyper-heuristic framework, a tabu search approach is employed to search for permutations of graph heuristics which are used for constructing timetables in exam and course timetabling problems. This underpins a multi-stage hyper-heuristic where the tabu search employs permutations upon a different number of graph heuristics in two stages. We study this graph-based hyper-heuristic approach within the context of exploring fundamental issues concerning the search space of the hyper-heuristic (the heuristic space) and the solution space. Such issues have not been addressed in other hyper-heuristic research. These approaches are tested on both exam and course benchmark timetabling problems and are compared with the fine-tuned bespoke state-of-theart approaches. The results are within the range of the best results reported in the literature. The approach described here represents a significantly more generally applicable approach than the current state of the art in the literature. Future work will extend this hyper-heuristic framework by employing methodologies which are applicable on a wider range of timetabling and scheduling problems. � 2005 Elsevier B.V. All rights reserved.

Investigating a hybrid metaheuristic for job shop rescheduling

Abstract: Previous research has shown that artificial immune systems can be used to produce robust schedules in a manufacturing environment. The main goal is to develop building blocks (antibodies) of partial schedules that can be used to construct backup solutions (antigens) when disturbances occur during production. The building blocks are created based upon underpinning ideas from artificial immune systems and evolved using a genetic algorithm (Phase I). Each partial schedule (antibody) is assigned a fitness value and the best partial schedules are selected to be converted into complete schedules (antigens). We further investigate whether simulated annealing and the great deluge algorithm can improve the results when hybridised with our artificial immune system (Phase II). We use ten fixed solutions as our target and measure how well we cover these specific scenarios.

Investigating a Hybrid Metaheuristic for Job Shop Rescheduling

Abstract: Previous research has shown that artificial immune systems can be used to produce robust schedules in a manufacturing environment. The main goal is to develop building blocks (antibodies) of partial schedules that can be used to construct backup solutions (antigens) when disturbances occur during production. The building blocks are created based upon underpinning ideas from artificial immune systems and evolved using a genetic algorithm (Phase I). Each partial schedule (antibody) is assigned a fitness value and the best partial schedules are selected to be converted into complete schedules (antigens). We further investigate whether simulated annealing and the great deluge algorithm can improve the results when hybridised with our artificial immune system (Phase II). We use ten fixed solutions as our target and measure how well we cover these specific scenarios.