733 research outputs found
Evolutionary Data Mining Design to Visualize the Examination Timetabling Data at a University: A First Round Development.
Examination scheduling ("timetabling") at a University
is a determined challenge. Allocating exam stipulate “time
slots" requires most advanced quantitative techniques. This study takes an alternate approach of applying the principles of data mining (DM) explicitly using undirected data mining, data preprocessing to get the patterns in data then understand the relationship between them
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
MetroNG: Computer-Aided Scheduling and Collision Detection
In this paper, we propose a formal model of the objects involved in a class of scheduling problems, namely in the classroom scheduling in universities which allow a certain degree of liberty in their curricula. Using the formal model, we present efficient algorithms for the detection of collisions of the involved objects and for the inference of a tree-like navigational structure in an interactive scheduling software allowing a selection of the most descriptive view of the scheduling objects. These algorithms were used in a real-world application called MetroNG; a visual interactive tool that is based on more than 10 years of experience we have in the field. It is currently used by the largest universities and colleges in the Czech Republic. The efficiency and usability of MetroNG suggests that our approach may be applied in many areas where multi-dimensionally structured data are presented in an interactive application
Fairness in examination timetabling: student preferences and extended formulations
Variations of the examination timetabling problem have been investigated by the research community for more than two decades. The common characteristic between all problems is the fact that the definitions and data sets used all originate from actual educational institutions, particularly universities, including specific examination criteria and the students involved. Although much has been achieved and published on the state-of-the-art problem modelling and optimisation, a lack of attention has been focussed on the students involved in the process. This work presents and utilises the results of an extensive survey seeking student preferences with regard to their individual examination timetables, with the aim of producing solutions which satisfy these preferences while still also satisfying all existing benchmark considerations. The study reveals one of the main concerns relates to fairness within the students cohort; i.e. a student considers fairness with respect to the examination timetables of their immediate peers, as highly important. Considerations such as providing an equitable distribution of preparation time between all student cohort examinations, not just a majority, are used to form a measure of fairness. In order to satisfy this requirement, we propose an extension to the state-of-the-art examination timetabling problem models widely used in the scientific literature. Fairness is introduced as a new objective in addition to the standard objectives, creating a multi-objective problem. Several real-world examination data models are extended and the benchmarks for each are used in experimentation to determine the effectiveness of a multi-stage multi-objective approach based on weighted Tchebyceff scalarisation in improving fairness along with the other objectives. The results show that the proposed model and methods allow for the production of high quality timetable solutions while also providing a trade-off between the standard soft constraints and a desired fairness for each student
DEM Timetabling Project ? Development/implementation of an algorithm to support the creation of timetables
This work presents the development of an algorithm to support the process of creating academic
timetables, specifically aimed at solving the University Course Timetabling Problem. To date, this
problem is solved manually in Instituto Superior de Engenharia do Porto, where professors and
engineers face the complex task of creating timetables based on schedules from previous years.
The proposed solution aimed to support the process of creating timetables at ISEP, reducing the
time and human resources required for this task. The developed algorithm uses an integer
programming approach and can consider a variety of constraints and preferences of both faculty
and students. It was designed to adapt and optimize the timetable creation process as needs evolve,
ensuring future demands can be easily accommodated.
The algorithm implementation was based on the Python programming language and the Pyomo
library, offering a flexible and efficient approach to optimizing resource allocation. Additionally, the
system is designed to import data from real-world sources, simplifying the integration of crucial
information.
The result assigned all the 128 one-hour classes among the week, presenting the faculty member,
the classroom assigned and the type of class according to each course. This research presents
feasible solutions that need improvement on the demanding conditions and restrictions imposed
by ISEP. The computational results obtained offered a significantly decrease in the time resource
used, compared to the manual work previously done
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 problems’ evaluation functions
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