The timetabling of lecturers, seminars, practical sessions and examinations is a core business process for academic institutions. A feasible timetable must satisfy hard constraints, an optimum timetable will additionally satisfy soft constraints, which are not absolutely essential. An Ant Colony based Timetabling Tool (ANCOTT) has been developed for solving timetabling problems. New variants of Ant Colony Optimisation (ACO) called Best-Worst Ant System (BWAS) and Best-Worst Ant Colony System (BWACS) were embedded in the ANCOTT program. Local Search (LS) strategies were developed and embedded into BWAS and BWACS to enhance their efficiency and to help find the best timetable with the lowest number of soft constraint violations. Statistical tools for experimental design and analysis were adopted to investigate the factors affecting the BWAS performance. Eight benchmarking instant problems were used for benchmarking the performance. The proposed LS enhanced both BWAS and BWACS performances by up to 70% but required longer execution time.

No abstract

This paper outlines the development of a new evolutionary algorithms based timetabling (EAT) tool for solving course scheduling problems that include a genetic algorithm (GA) and a memetic algorithm (MA). Reproduction processes may generate infeasible solutions. Previous research has used repair processes that have been applied after a population of chromosomes has been generated. This research developed a new approach which (i) modified the genetic operators to prevent the creation of infeasible solutions before chromosomes were added to the population; (ii) included the clonal selection algorithm (CSA); and the elitist strategy (ES) to improve the quality of the solutions produced. This approach was adopted by both the GA and MA within the EAT. The MA was further modified to include hill climbing local search. The EAT program was tested using 14 benchmark timetabling problems from the literature using a sequential experimental design, which included a fractional factorial screening experiment. Experiments were conducted to (i) test the performance of the proposed modified algorithms; (ii) identify which factors and interactions were statistically significant; (iii) identify appropriate parameters for the GA and MA; and (iv) compare the performance of the various hybrid algorithms. The genetic algorithm with modified genetic operators produced an average improvement of over 50%.

No abstract