104 research outputs found
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
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
A bi-criteria simulated annealing algorithm for the robust university course timetabling problem
A bi-criteria version of the curriculum-based university timetabling
problem of ITC-2007 is solved using a multi-objective simulated annealing
(MOSA) algorithm that identifies an approximation to the optimal Pareto
front. The two criteria are the penalty function as defined in ITC-2007 and
a robustness function. The robustness function assumes one disruption occurs
in the form of a period of an event (lecture) becoming infeasible for that
event. The parameters of the MOSA algorithm are set using the Iterated FRace
algorithm and then its performance is tested against a hybrid MOGA
algorithm developed by the authors. The results show that MOSA provides
better approximation fronts than the hybrid MOGA
Educational timetabling: Problems, benchmarks, and state-of-the-art results
We propose a survey of the research contributions on the field of Educational Timetabling with a specific focus on “standard” formulations and the corresponding benchmark instances. We identify six of such formulations and we discuss their features, pointing out their relevance and usability. Other available formulations and datasets are also reviewed and briefly discussed. Subsequently, we report the main state-of-the-art results on the selected benchmarks, in terms of solution quality (upper and lower bounds), search techniques, running times, and other side settings
Hybrid meta-heuristics for combinatorial optimization
Combinatorial optimization problems arise, in many forms, in vari- ous aspects of everyday life. Nowadays, a lot of services are driven by optimization algorithms, enabling us to make the best use of the available resources while guaranteeing a level of service. Ex- amples of such services are public transportation, goods delivery, university time-tabling, and patient scheduling.
Thanks also to the open data movement, a lot of usage data about public and private services is accessible today, sometimes in aggregate form, to everyone. Examples of such data are traffic information (Google), bike sharing systems usage (CitiBike NYC), location services, etc. The availability of all this body of data allows us to better understand how people interacts with these services. However, in order for this information to be useful, it is necessary to develop tools to extract knowledge from it and to drive better decisions. In this context, optimization is a powerful tool, which can be used to improve the way the available resources are used, avoid squandering, and improve the sustainability of services.
The fields of meta-heuristics, artificial intelligence, and oper- ations research, have been tackling many of these problems for years, without much interaction. However, in the last few years, such communities have started looking at each other’s advance- ments, in order to develop optimization techniques that are faster, more robust, and easier to maintain. This effort gave birth to the fertile field of hybrid meta-heuristics.openDottorato di ricerca in Ingegneria industriale e dell'informazioneopenUrli, Tommas
Bi-Criteria Simulated Annealing Algorithms for the Robust University Course Timetabling Problem
A bi-criteria version of the curriculum-based university timetabling
problem of ITC-2007 is solved using a multi-objective simulated annealing
(MOSA) algorithm that identifies an approximation to the optimal Pareto
front. The two criteria are the penalty function as defined in ITC-2007 and
a robustness function. The robustness function assumes one disruption occurs
in the form of a period of an event (lecture) becoming infeasible for that
event. The parameters of the MOSA algorithm are set using the Iterated FRace
algorithm and then its performance is tested against a hybrid MOGA
algorithm developed by the authors. The results show that MOSA provides
better approximation fronts than the hybrid MOGA
Comments on: An overview of curriculum-based course timetabling
1noopenopenSchaerf, AndreaSchaerf, Andre
Automated university lecture timetable using Heuristic Approach
There are different approaches used in automating course timetabling problem in tertiary institution. This paper present a combination of genetic algorithm (GA) and simulated annealing (SA) to have a heuristic approach (HA) for solving course timetabling problem in Federal University Wukari (FUW). The heuristic approach was implemented considering the soft and hard constraints and the survival for the fittest. The period and space complexity was observed. This helps in matching the number of rooms with the number of courses.
Keywords: Heuristic approach (HA), Genetic algorithm (GA), Course Timetabling, Space Complexity
Aspects of computerised timetabling
This research considers the problem of constructing high school timetables using a
computer. In the majority of high schools, termly or yearly timetables are still
being produced manually. Constructing a timetable is a hard and time consuming
task which is carried out repeatedly thus a computer program for assisting with this
problem would be of great value. This study is in three parts. First. an overall
analysis of the problem is undertaken to provide background knowledge and to
identify basic principles in the construction of a school timetable. The
characteristics of timetabling problems are identified and the necessary data for the
construction of a timetable is identified. The first part ends with the production of
a heuristic model for generating an initial solution that satisfies all the hard
constraints embodied in the curriculum requirements.
The second stage of the research is devoted to designing a heuristic model for
solving a timetable problem with hard and medium constraints. These include
constraints like the various numbers of common periods, double periods and
reducing the repeated allocation of a subject within any day. The approaches taken
are based on two recently developed techniques, namely tabu search and simulated
annealing. Both of these are used and comparisons of their efficiency are
provided. The comparison is based on the percentage fulfilment of the hard and
medium requirements.
The third part is devoted to one of the most difficult areas in timetable
construction, that is the softer requirements which are specific to particular schools
and whose satisfaction is not seen as essential. This section describes the
development of an expert system based on heuristic production rules to satisfy a
range of soft requirements. The soft requirements are studied and recorded as
rules and a heuristic solution is produced for each of the general requirements.
Different levels of rule are developed, from which the best possible solution to a
particular timetable problem is expertly produced.
Finally, possible extensions of the proposed method and its application to other
types of the timetabling problem are discussed
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