64 research outputs found
A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling
Copyright @ Springer Science + Business Media. All rights reserved.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 optimisation 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.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02
Incorporating Machine Learning to Evaluate Solutions to the University Course Timetabling Problem
Evaluating solutions to optimization problems is arguably the most important step for heuristic algorithms, as it is used to guide the algorithms towards the optimal solution in the solution search space. Research has shown evaluation functions to some optimization problems to be impractical to compute and have thus found surrogate less expensive evaluation functions to those problems. This study investigates the extent to which supervised learning algorithms can be used to find approximations to evaluation functions for the university course timetabling problem. Up to 97 percent of the time, the traditional evaluation function agreed with the supervised learning regression model on the result of comparison of the quality of pair of solutions to the university course timetabling problem, suggesting that supervised learning regression models can be suitable alternatives for optimization problemsā evaluation functions
Design, Engineering, and Experimental Analysis of a Simulated Annealing Approach to the Post-Enrolment Course Timetabling Problem
The post-enrolment course timetabling (PE-CTT) is one of the most studied
timetabling problems, for which many instances and results are available. In
this work we design a metaheuristic approach based on Simulated Annealing to
solve the PE-CTT. We consider all the different variants of the problem that
have been proposed in the literature and we perform a comprehensive
experimental analysis on all the public instances available. The outcome is
that our solver, properly engineered and tuned, performs very well on all
cases, providing the new best known results on many instances and
state-of-the-art values for the others
Hybrid harmony search with great deluge for UUM CAS curriculum based course timetabling
Producing university course timetabling is a tough and complicated task due to higher number of courses and constraints.The process usually consisted of satisfying a set of hard constraints so as a feasible solution can be obtained.It then continues with the process of optimizing (minimizing) the soft constraints in order to produce a good quality timetable. In this paper, a hybridization of harmony search with a great deluge is proposed to optimize the soft constraints.Harmony search comprised of two main operators such as memory consideration and random consideration operator.The great deluge was applied on the random consideration operator. The proposed approach was also adapted on curriculum-based course timetabling problems of College of Arts and Sciences, Universiti Utara Malaysia (UUM CAS).The result shows that the quality of timetable of UUM CAS produced by the proposed approach is superior than the quality of timetable produced using the current software package
Improved local search approaches to solve the post enrolment course timetabling problem
In this work, we are addressing the post enrollment course timetabling (PE-CTT) problem. We combine different local search algorithms into an iterative two stage procedure. In the first stage, Tabu Search with Sampling and Perturbation (TSSP) is used to generate feasible solutions. In the second stage, we propose an improved variant of Simulated Annealing (SA), which we call Simulated Annealing with Reheating (SAR), to improve the solution quality of feasible solutions. SAR has three features: a novel neighborhood examination scheme, a new way of estimating local optima and a reheating scheme. SAR eliminates the need for extensive tuning as is often required in conventional SA. The proposed methodologies are tested on the three most studied datasets from the scientific literature. Our algorithms perform well and our results are competitive, if not better, compared to the benchmarks set by the state of the art methods. New best known results are provided for many instances
An effective hybrid local search approach for the post enrolment course timetabling problem
We address the post enrolment course timetabling (PE-CTT) problem in this paper. PE-CTT is known as an NP-hard problem that focuses on finding an efficient allocation of courses onto a finite number of time slots and rooms. It is one of the most challenging resources allocation problems faced by universities around the world. This work proposes a two-phase hybrid local search algorithm to address the PE-CTT problem. The first phase focuses on finding a feasible solution, while the second phase tries to minimize the soft constraint violations of the generated feasible solution. For the first phase, we propose a hybrid of Tabu Search with Sampling and Perturbation with Iterated Local Search. We test the proposed methodology on the hardest cases of PE-CTT benchmarks. The hybrid algorithm performs well and our results are superior compared to the recent methods in finding feasible solutions. For the second phase, we propose an algorithm called Simulated Annealing with Reheating (SAR) with two preliminary runs (SAR-2P). The SAR algorithm is used to minimize the soft constraint violations by exploiting information collected from the preliminary runs. We test the proposed methodology on three publicly available datasets. Our algorithm is competitive with the standards set by the recent methods. In total, the algorithm attains new best results for 3 cases and new best mean results for 7 cases. Furthermore, it is scalable when the execution time is extended
An investigation of Monte Carlo tree search and local search for course timetabling problems
The work presented in this thesis focuses on solving course timetabling problems, a variant of education timetabling. Automated timetabling is a popular topic among researchers and practitioners because manual timetable construction is impractical, if not impossible, as it is known to be NP-hard.
A two-stage approach is investigated. The ļ¬rst stage involves ļ¬nding feasible solutions. Monte Carlo Tree Search (MCTS) is utilized in this stage. As far as we are aware, it is used for the ļ¬rst time in addressing the timetabling problem. It is a relatively new search method and has achieved breakthrough in the domain of games particularly Go. Several enhancements are attempted on MCTS such as heuristic based simulations and pruning. We also compare the eļ¬ectiveness of MCTS with Graph Coloring Heuristic (GCH) and Tabu Search (TS) based methods. Initial ļ¬ndings show that a TS based method is more promising, so we focus on improving TS. We propose an algorithm called Tabu Search with Sampling and Perturbation (TSSP). Among the enhancements that we introduced are event sampling, a novel cost function and perturbation. Furthermore, we hybridize TSSP with Iterated Local Search (ILS).
The second stage focuses on improving the quality of feasible solutions. We propose a variant of Simulated Annealing called Simulated Annealing with Reheating (SAR). SAR has three features: a novel neighborhood examination scheme, a new way of estimating local optima and a reheating scheme. The rigorous setting of initial and end temperature in conventional SA is bypassed in SAR. Precisely, reheating and cooling were applied at the right time and level, thus saving time allowing the search to be performed eļ¬ciently. One drawback of SAR is having to preset the composition of neighborhood structures for the datasets. We present an enhanced variant of the SAR algorithm called Simulated Annealing with Improved Reheating and Learning (SAIRL). We propose a reinforcement learning based method to obtain a suitable neighborhood structure composition for the search to operate eļ¬ectively. We also propose to incorporate the average cost changes into the reheated temperature function. SAIRL eliminates the need for tuning parameters in conventional SA as well as neighborhood structures composition in SAR.
Experiments were tested on four publicly available datasets namely Socha, International Timetabling Competition 2002 (ITC02), International Timetabling Competition 2007 (ITC07) and Hard. Our results are better or competitive when compared with other state of the art methods where new best results are obtained for many instances
An investigation of Monte Carlo tree search and local search for course timetabling problems
The work presented in this thesis focuses on solving course timetabling problems, a variant of education timetabling. Automated timetabling is a popular topic among researchers and practitioners because manual timetable construction is impractical, if not impossible, as it is known to be NP-hard.
A two-stage approach is investigated. The ļ¬rst stage involves ļ¬nding feasible solutions. Monte Carlo Tree Search (MCTS) is utilized in this stage. As far as we are aware, it is used for the ļ¬rst time in addressing the timetabling problem. It is a relatively new search method and has achieved breakthrough in the domain of games particularly Go. Several enhancements are attempted on MCTS such as heuristic based simulations and pruning. We also compare the eļ¬ectiveness of MCTS with Graph Coloring Heuristic (GCH) and Tabu Search (TS) based methods. Initial ļ¬ndings show that a TS based method is more promising, so we focus on improving TS. We propose an algorithm called Tabu Search with Sampling and Perturbation (TSSP). Among the enhancements that we introduced are event sampling, a novel cost function and perturbation. Furthermore, we hybridize TSSP with Iterated Local Search (ILS).
The second stage focuses on improving the quality of feasible solutions. We propose a variant of Simulated Annealing called Simulated Annealing with Reheating (SAR). SAR has three features: a novel neighborhood examination scheme, a new way of estimating local optima and a reheating scheme. The rigorous setting of initial and end temperature in conventional SA is bypassed in SAR. Precisely, reheating and cooling were applied at the right time and level, thus saving time allowing the search to be performed eļ¬ciently. One drawback of SAR is having to preset the composition of neighborhood structures for the datasets. We present an enhanced variant of the SAR algorithm called Simulated Annealing with Improved Reheating and Learning (SAIRL). We propose a reinforcement learning based method to obtain a suitable neighborhood structure composition for the search to operate eļ¬ectively. We also propose to incorporate the average cost changes into the reheated temperature function. SAIRL eliminates the need for tuning parameters in conventional SA as well as neighborhood structures composition in SAR.
Experiments were tested on four publicly available datasets namely Socha, International Timetabling Competition 2002 (ITC02), International Timetabling Competition 2007 (ITC07) and Hard. Our results are better or competitive when compared with other state of the art methods where new best results are obtained for many instances
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