Abstract
In this study, we investigate an adaptive decomposition and ordering strategy that automatically divides examinations into difficult and easy sets for constructing an examination timetable. The examinations in the difficult set are considered to be hard to place and hence are listed before the ones in the easy set in the construction process. Moreover, the examinations within each set are ordered using different strategies based on graph colouring heuristics. Initially, the examinations are placed into the easy set. During the construction process, examinations that cannot be scheduled are identified as the ones causing infeasibility and are moved forward in the difficult set to ensure earlier assignment in subsequent attempts. On the other hand, the examinations that can be scheduled remain in the easy set. Within the easy set, a new subset called the boundary set is introduced to accommodate shuffling strategies to change the given ordering of examinations. The proposed approach, which incorporates different ordering and shuffling strategies, is explored on the Carter benchmark problems. The empirical results show that the performance of our algorithm is broadly comparable to existing constructive approaches.
Highlights
The focus of this study is the university examination timetabling problem
It is observed that by merging or swapping the boundary set with the difficult set we could improve solution quality
A stochastic component based on roulette wheel selection is embedded into the approach in order to shuffle the order of examinations
Summary
The focus of this study is the university examination timetabling problem. Principally, the examination timetabling problem is concerned with the scheduling of a list of examinations into a restricted number of time-slots while satisfying a defined set of constraints. Graph colouring heuristics have been ‘customised’ with adaptive approaches to order the examinations based on their difficulty of timetabling (Burke and Newall 2004). This utilises the framework of ‘squeaky wheel optimisation’ (Joslin and Clements 1999). In 2009, Abdul Rahman et al (2009) extended this study by introducing more strategies for choosing an examination to be scheduled and the time-slots In another adaptive approach, Casey and Thompson (2003) developed a GRASP algorithm for solving the examination timetabling problems. The study by Qu and Burke (2007) describes an adaptive decomposition approach for constructing an examination timetable.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have