Domain transformation approach to deterministic optimization of examination timetables

In this paper we introduce a new optimization method for the examinations scheduling problem. Rather than attempting direct optimization of assignments of exams to specific time-slots, we perform permutations of slots and reassignments of exams upon the feasible (but not optimal) schedules obtained by the standard graph colouring method with Largest Degree ordering. The proposed optimization methods have been evaluated on the University of Toronto, University of Nottingham and International Timetabling Competition (ITC2007) datasets. It is shown that the proposed method delivers competitive results compared to other constructive methods in the timetabling literature on both the Nottingham and Toronto datasets, and it maintains the same optimization pattern of the solution improvement on the ITC2007 dataset. A deterministic pattern obtained for all benchmark datasets, makes the proposed method more understandable to the users

Incorporating capacitative constraint to the preference-based conference scheduling via domain transformation approach

No AbstractKeywords: conference scheduling; domain transformation approach; capacity optimizatio

Solving the preference-based conference scheduling problem through domain transformation approach

Conference scheduling can be quite a simple and straightforward problem if the number of papers to be scheduled is small.However, the problem can be very challenging and complex if the number of papers is large and various additional constraints need to be satisfied.Conference scheduling with regard to satisfying participants’ preferences can be understood as to generate schedule to minimize the clashes between slots or sessions that participants are interested to attend.Motivated by the current research trend in maximizing participants’ satisfactions, the study looks at the possibility of scheduling papers to sessions without any conflict by considering preferences by participants.In this research, preferences refer to the papers chosen by participants that they would like to attend its’ presentations sessions.Domain Transformation Approach (DTA), which has produced very encouraging results in our previous works, is used in this study to solve preference-based conference scheduling problem. The purpose of utilizing the method is to test the generality and universality of the approach in producing feasible schedule, given a different scheduling problem.The results obtained confirm that DTA efficiently generated feasible schedule which satisfies hard constraints and also fulfills all the preferences.With the generated schedule, all participants are able to attend their sessions of interest.In the future work, additional constraints will be taken into account in optimizing the schedules, for example balancing the number of papers assigned to each timeslot, and minimizing assignment of presenters to different timeslots.Other datasets could also be tested in order to test the generality of the proposed approach

Transformation of the university examination timetabling problem space through data pre-processing

This research investigates Examination Timetabling or Scheduling, with the aim of producing good quality, feasible timetables that satisfy hard constraints and various soft constraints. A novel approach to scheduling, that of transformation of the problem space, has been developed and evaluated for its effectiveness. The examination scheduling problem involves many constraints due to many relationships between students and exams, making it complex and expensive in terms of time and resources. Despite the extensive research in this area, it has been observed that most of the published methods do not produce good quality timetables consistently due to the utilisation of random-search. In this research we have avoided random-search and instead have proposed a systematic, deterministic approach to solving the examination scheduling problem. We pre-process data and constraints to generate more meaningful aggregated data constructs with better expressive power that minimise the need for cross-referencing original student and exam data at a later stage. Using such aggregated data and custom-designed mechanisms, the timetable construction is done systematically, while assuring its feasibility. Later, the timetable is optimized to improve the quality, focusing on maximizing the gap between consecutive exams. Our solution is always reproducible and displays a deterministic optimization pattern on all benchmark datasets. Transformation of the problem space into new aggregated data constructs through pre-processing represents the key novel contribution of this research

Transformation of the university examination timetabling problem space through data pre-processing

This research investigates Examination Timetabling or Scheduling, with the aim of producing good quality, feasible timetables that satisfy hard constraints and various soft constraints. A novel approach to scheduling, that of transformation of the problem space, has been developed and evaluated for its effectiveness. The examination scheduling problem involves many constraints due to many relationships between students and exams, making it complex and expensive in terms of time and resources. Despite the extensive research in this area, it has been observed that most of the published methods do not produce good quality timetables consistently due to the utilisation of random-search. In this research we have avoided random-search and instead have proposed a systematic, deterministic approach to solving the examination scheduling problem. We pre-process data and constraints to generate more meaningful aggregated data constructs with better expressive power that minimise the need for cross-referencing original student and exam data at a later stage. Using such aggregated data and custom-designed mechanisms, the timetable construction is done systematically, while assuring its feasibility. Later, the timetable is optimized to improve the quality, focusing on maximizing the gap between consecutive exams. Our solution is always reproducible and displays a deterministic optimization pattern on all benchmark datasets. Transformation of the problem space into new aggregated data constructs through pre-processing represents the key novel contribution of this research