A Comparison of the Performance of Different Metaheuristics on the Timetabling Problem

Rossi-Doria, Olivia; Sampels, Michaël; Birattari, Mauro; Chiarandini, Marco; Dorigo, Marco; Gambardella, Luca Maria; Knowles, Joshua; Manfrin, Max; Mastrolilli, Monaldo; Paechter, Ben; Paquete, Luís; Stützle, Thomas

doi:10.1007/978-3-540-45157-0_22

Cited by 100 publications

(133 citation statements)

References 14 publications

Supporting

Mentioning

129

Contrasting

Order By: Relevance

“…[3]), but contains more information than the exclusive use of room/event pheromones (e.g. [2]). Our pheromone concept is further supported by the observation that the assignment of a room is less critical in comparison to the assignment to a timeslot.…”

mentioning

confidence: 99%

Solving the post enrolment course timetabling problem by ant colony optimization

et al. 2012

View full text Add to dashboard Cite

mentioning

confidence: 99%

Solving the post enrolment course timetabling problem by ant colony optimization

et al. 2012

View full text Add to dashboard Cite

“…Various combinations of local area and global area based algorithms have been reported to solve timetabling problems in the literature [15,51,57,36,3]. In addition, it is also being increasingly realized that EAs without incorporation of problem-specific knowledge do not perform as well as mathematical programming based algorithms on certain classes of timetabling problems [13].…”

Section: Related Workmentioning

confidence: 99%

“…Several researchers have used GAs to solve course timetabling problems [5,25,40,48]. Rossi-Doria et al [51] compared different meta-heuristics to solve the UCTP. They concluded that conventional GAs do not give good results among a number of approaches developed for the UCTP.…”

Section: Introductionmentioning

confidence: 99%

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

Jat

Yang

2010

J Sched

View full text Add to dashboard Cite

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 optimization 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.

show abstract

“…In recent years, several researchers have tackled the course timetabling problem, particulary the set of 11 instances proposed by Socha et al [14]. Among the algorithms proposed there are: a MAX-MIN ant system by Socha et al [14]; a tabu search hyper-heuristic strategy by Burke et al [7]; an evolutionary algorithm, ant colony optimisation, iterated local search, simulated annealing and tabu search by Rossi-Doria et al [13]; fuzzy multiple heuristic ordering by Asmuni et al [5]; variable neighbourhood search by Abdullah et al [1]; iterative improvement with composite neighbourhoods by Abdullah et al [2,4]; a graphbased hyper-heuristic by Burke et al [9] and a hybrid evolutionary algorithm by Abdullah et al [3].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Non-linear Great Deluge for University Course Timetabling

Landa-Silva

Obit

2009

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. This paper presents a hybrid evolutionary algorithm to tackle university course timetabling problems. The proposed approach is an extension of a non-linear great deluge algorithm in which evolutionary operators are incorporated. First, we generate a population of feasible solutions using a tailored process that incorporates heuristics for graph colouring and assignment problems. That initialisation process is capable of producing feasible solutions even for the large and most constrained problem instances. Then, the population of feasible timetables is subject to a steady-state evolutionary process that combines mutation and stochastic local search. We conduct experiments to evaluate the performance of the proposed hybrid algorithm and in particular, the contribution of the evolutionary operators. Our results show that the hybrid between non-linear great deluge and evolutionary operators produces very good results on the instances of the university course timetabling problem tackled here.

show abstract

A Comparison of the Performance of Different Metaheuristics on the Timetabling Problem

Cited by 100 publications

References 14 publications

Solving the post enrolment course timetabling problem by ant colony optimization

Solving the post enrolment course timetabling problem by ant colony optimization

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

Evolutionary Non-linear Great Deluge for University Course Timetabling

Contact Info

Product

Resources

About