Selecting quality initial random seed for metaheuristic pproaches: a case of timetabling problem

The Timetabling Problem is a combinatorial optimization problem. The University Course Timetabling Problems (UCTP) deals with the scheduling of the teaching program. Metaheuristic techniques have been very successful in a wide range of timetabling problem including UCTP. The performance of metaheuristic over UCTP is measured by quality timetable that is no violation of hard constraints and the lowest number of soft constraint violated. The stochastic natures of the metaheuristic approaches make it difficult to predict the quality of end result produced. Therefore the initial quality solutions are one of the important factors contributed to success of metaheuristic approaches in solving optimization problem particularly UCTP. This paper analyzes the effect of different random seed over metaheuristic performance. Techniques for selecting quality random seeding as an input for metaheuristic algorithm to solve university course timetabling are presented. The main objective is to obtain quality initial solution without much effort to construct difficult heuristic. The result obtained gives us opportunity to choose quality initial solution with less effort

Meta-heuristic approaches for the University course timetabling problem

Course timetabling is an ongoing challenge that universities face all around the world. This combinatorial optimization task involves allocating a set of events into finite time slots and rooms while attempting to satisfy a set of predefined constraints. Given the high number of constraints and the large solution space to be explored, the University Course Timetabling Problem (UCTP) is classified as an NP-hard problem. Meta-heuristic approaches have been commonly applied to this problem in the literature and have achieved high performance on benchmark datasets. This survey paper provides a comprehensive and systematic review of these approaches in the UCTP. It reviews, summarizes, and categorizes the approaches, and introduces a classification for hybrid meta-heuristic methods. Furthermore, it critically analyzes the benefits and limitations of the methods. It also presents challenges, gaps, and possible future work

An Assignment Problem and Its Application in Education Domain: A Review and Potential Path

This paper presents a review pertaining to assignment problem within the education domain, besides looking into the applications of the present research trend, developments, and publications. Assignment problem arises in diverse situations, where one needs to determine an optimal way to assign n subjects to m subjects in the best possible way.With that, this paper classified assignment problems into two, which are timetabling problem and allocation problem. The timetabling problem is further classified into examination, course, and school timetabling problems, while the allocation problem is divided into student-project allocation, new student allocation, and space allocation problems. Furthermore, the constraints, which are of hard and soft constraints, involved in the said problems are briefly elaborated.In addition, this paper presents various approaches to address various types of assignment problem. Moreover, direction and potential paths of problem solving based on the latest trend of approaches are also highlighted.As such, this review summarizes and records a comprehensive survey regarding assignment problem within education domain, which enhances one's understanding concerning the varied types of assignment problems, along with various approaches that serve as solution

High school timetabling using selection hyperheuristics

Educational timetabling is a hard, challenging real world optimization problem which has been interest to researchers in the fields of optimization and artificial intelligence. This problem has many variants including examination timetabling, university course timetabling, and high school timetabling. In this study we focus on high school timetabling problem which requires scheduling of resources and events in time slots under a set of constraints. Selection hyper-heuristic is a high level search methodology that operates on the space of a fixed set of low level heuristics that operates directly on the space of the solution. The selection hyper-heuristic has two main components: heuristic selection, and move acceptance. A heuristic selection selects a low level heuristic from a set of low level heuristics and applies it to generate a solution. The acceptance method decides to accept or reject the new solution. In this study we present a set of effective selection hyper-heuristics that mixes and controls a set of perturbative low level heuristics to present a solution to the high school timetabling problem. A set of selection methods combined with a set of move acceptance methods were tested and their performance was analysed using different statistical measures on a set of instances of the high school timetabling problem by the aim of comparing and determining the performance variations between the selection hyper-heuristics and choosing the best approach to solve the high school timetabling problem. The empirical results of the study proved the effectiveness of the approach in solving the high school problem achieving results that outperformed most of the best known approaches

A memetic algorithm for the university course timetabling problem

This article is posted here with permission from IEEE - Copyright @ 2008 IEEEThe 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

Non-linear great deluge with learning mechanism for solving the course timetabling problem

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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

Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem

We consider the university course timetabling problem, which is one of the most studied problems in educational timetabling. In particular, we focus our attention on the formulation known as the curriculum-based course timetabling problem, which has been tackled by many researchers and for which there are many available benchmarks. The contribution of this paper is twofold. First, we propose an effective and robust single-stage simulated annealing method for solving the problem. Secondly, we design and apply an extensive and statistically-principled methodology for the parameter tuning procedure. The outcome of this analysis is a methodology for modeling the relationship between search method parameters and instance features that allows us to set the parameters for unseen instances on the basis of a simple inspection of the instance itself. Using this methodology, our algorithm, despite its apparent simplicity, has been able to achieve high quality results on a set of popular benchmarks. A final contribution of the paper is a novel set of real-world instances, which could be used as a benchmark for future comparison