Abstract The university course timetabling problem is a combinatorial optimisation problem in which a set of events has to be scheduled in time slots and located in suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper proposes a genetic algorithm with a guided search strategy and a local search technique for the university course timetabling problem. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from previous good individuals. The local search technique is used to improve the quality of individuals. The proposed genetic algorithm is tested on a set of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed genetic algorithm is able to produce promising results for the university course timetabling problem. 1

Labeling is crucial in the performance of solving timetabling problems with constraint programming. Traditionally, labeling strategies are based on static and dynamic information about variables and their domains, and selecting variables and values to assign. However, the size of combinatorial problems tractable by these techniques is limited. In this paper, we present a real problem solved with constraint programming using programmed search based on the knowledge about the solution structure as a starting point for classical propagation and labeling techniques to find a feasible solution. For those problems in which solutions are close to the seed because of its structure, propagation and labeling can reach a first solution within a small response time. We apply our approach to a real timetabling problem, and we tackle its implementation with two different languages, OPL and T OY, using the constraint programming paradigm over finite domains. While OPL is a commercial, algebraic, and specific-purpose constraint programming language, T OY is a prototype of a general-purpose constraint functional logic programming language. We present the specification of the problem, its implementation with both languages, and a comparative performance analysis.

In this abstract we present our work to tackle the problem of Post Enrolment Course Timetabling as specified for the International Timetabling Competition 2007 (ITC2007), competition track 2. The heuristic procedure is based on Ant Colony Optimization (ACO) where artificial ants successively construct solutions based on pheromones (stigmergy) and local information; see [1] for a more general introduction to ACO. Representation. In our algorithm, which can be more specifically classified as Ant System (AS), ants are basically assigning events to rooms and timeslots which represent the based on two kinds of pheromone denoted by τ s ij and τ r ik probabilities of assigning an event i to slot j and room k, respectively. The decision to store pheromone information in this way is a key-feature of the algorithm, as it avoids the usage of a much larger data structure implied by a more traditional encoding using individual pheromone values for all slot/room/event combinations (see e.g. [3]), but contains more information than the exclusive use of

Abstract- Course Timetabling is a complex problem, happening at the beginning of every semester at universities. In this problem, one of the most important issues is variety of constraints, which results in different ways of timetabling in different universities. Comparing to exact methods which take lots of time to solve UCTP, metaheuristic methods produce a feasible solution within reasonable computation time. In this paper, a hybrid method is presented, which is based on combination of improved Memetic and Simulated Annealing Algorithms. Using Simulated Annealing Algorithm as the local search routine increases exploiting ability of Memetic Algorithm. Also, modifying Crossover operator of Memetic Algorithm and creating initial population by a heuristic-based method improves this algorithm. In order to improve produced chromosomes and decreasing the number of violation of the constraints, a new operator is designed and added to Memetic Algorithm called improvement operator. With comparing the results of this method and some modern methods using standard data, efficiency of this method is clear.

Abstract 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 slotsand locatedin 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 searchstrategyand somelocalsearchtechniques,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 takenfromtheinternationaltimetablingcompetition 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.

The design of course timetables for academic institutions is a very hectic job due to the exponential number of possible feasible timetables with respect to the problem size. This process involves lots of constraints that must be respected and a huge search space to be explored, even if the size of the problem input is not significantly large. On the other hand, the problem itself does not have a widely approved definition, since different institutions face different variations of the problem. This paper presents a memetic algorithm that integrates two local search methods into the genetic algorithm for solving the university course timetabling problem (UCTP). These two local search methods use their exploitive search ability to improve the explorative search ability of genetic algorithms. The experimental results indicate that the proposed memetic algorithm is efficient for solving the UCTP. 1