A guided search non-dominated sorting genetic algorithm for the multi-objective university course timetabling problem

Copyright @ Springer-Verlag Berlin Heidelberg 2011.The university course timetabling problem is a typical combinatorial optimization problem. This paper tackles the multi-objective university course timetabling problem (MOUCTP) and proposes a guided search non-dominated sorting genetic algorithm to solve the MOUCTP. The proposed algorithm integrates a guided search technique, which uses a memory to store useful information extracted from previous good solutions to guide the generation of new solutions, and two local search schemes to enhance its performance for the MOUCTP. The experimental results based on a set of test problems show that the proposed algorithm is efficient for solving the MOUCTP

A collaborative approach for solving the university course timetabling problem

This work proposes a collaborative approach for solving the university course timetabling problem (UCTP). A prototype was developed and used for a computer science course at the Federal Fluminense University in Brazil. The main idea is that students, professors, and course coordinators contribute collaboratively to course timetabling through an app. These contributions employ heuristics, which is responsible for timetabling to improve the solution to the problem. Results and future works are described herein

Decomposition, Reformulation, and Diving in University Course Timetabling

In many real-life optimisation problems, there are multiple interacting components in a solution. For example, different components might specify assignments to different kinds of resource. Often, each component is associated with different sets of soft constraints, and so with different measures of soft constraint violation. The goal is then to minimise a linear combination of such measures. This paper studies an approach to such problems, which can be thought of as multiphase exploitation of multiple objective-/value-restricted submodels. In this approach, only one computationally difficult component of a problem and the associated subset of objectives is considered at first. This produces partial solutions, which define interesting neighbourhoods in the search space of the complete problem. Often, it is possible to pick the initial component so that variable aggregation can be performed at the first stage, and the neighbourhoods to be explored next are guaranteed to contain feasible solutions. Using integer programming, it is then easy to implement heuristics producing solutions with bounds on their quality. Our study is performed on a university course timetabling problem used in the 2007 International Timetabling Competition, also known as the Udine Course Timetabling Problem. In the proposed heuristic, an objective-restricted neighbourhood generator produces assignments of periods to events, with decreasing numbers of violations of two period-related soft constraints. Those are relaxed into assignments of events to days, which define neighbourhoods that are easier to search with respect to all four soft constraints. Integer programming formulations for all subproblems are given and evaluated using ILOG CPLEX 11. The wider applicability of this approach is analysed and discussed.Comment: 45 pages, 7 figures. Improved typesetting of figures and table

A hybrid algorithm for university course timetabling problem

A hybrid algorithm combining the genetic algorithm with the iterated local search algorithm is developed for solving university course timetabling problem. This hybrid algorithm combines the merits of genetic algorithm and iterated local search algorithm for its convergence to global optima at the same time avoiding being get trapped into local optima. This leads to intensification of the involved search space for solutions. It is applied on a number of benchmark university course timetabling problem instances of various complexities. Keywords: timetabling, optimization, metaheuristics, genetic algorithm, iterative local searc

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

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

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

ASYNCHRONOUS ISLAND MODEL GENETIC ALGORITHM FOR UNIVERSITY COURSE TIMETABLING

ABSTRAKSI: Penjadwalan merupakan masalah kombinatorial kompleks yang diklasifikasikan sebagai NP - Hard . Masalah penjadwalan kuliah universitas (MPKU) mirip dengan masalah penjadwalan pada umumnya dengan beberapa bagian yang unik. MPKU adalah permasalahan penjadwalan dimana kita harus melakukan penjadwalan untuk pertemuan perkuliahan ke dalam slot waktu dan ruang tertentu dengan mempertimbangkan batasan keras (hard constrain) dan lunak (soft constraint) . Telkom University memiliki masala h yang hampir mirip dengan penjadwalan tersebut. Solusi saat ini dengan informed genetic algorithm untuk Telkom Universit y MPKU masih memiliki masalah waktu eksekusi .sland Model Genetic Algorithm digunakan dalam tesis ini untuk memecahkan masalah terseb ut . Ide tesis ini adalah membuat model pertukaran Individu untuk men distribusikan i ndividu lokal terbaik sebuah pulau dengan pulau lain. Island Model GA dapat membuat jadwal kuliah universitas dalam waktu yang masih dapat dipertimbangkan. Model terdistribusi ini dapat berjalan lebih cepat daripada model tunggal menurunkan pelanggaran batasan untuk mencapai nilai fitness yang optim um . Hal ini dapat terjadi karena model ini dapat keluar dari optimum lokal dengan lebih mudah. Island Model GA bahkan dapat menghasilkan akurasi yang baik untuk dataset Universitas Telkom (99,74%) dan akurasi yang cukup untuk dataset Purdue (96,80%) pada penjadwalan level mahasiswa.Kata Kunci : penjadwalan , penjadwalan kuliah universitas , informed genetic algorithm, island model genetic algorithmABSTRACT: Timetabling is a complex combinatorial problem classified as NP - Hard. University course timetabling problem (UCTP) is similar to other timetabling problems with some additional unique parts. UCTP involves assigning lecture events to timeslots and rooms subject to a variety of hard and soft constraints. Telkom University has almost similar problem with it s course timetabling. The current solution with Informed Genetic Algorithm for Telkom University UCTP still has the time consuming problem.sland Model informed Genetic Algorithm was used in this thesis to solve this problem. The idea of this thesis is m aking distributed model exchanges an island‟s local best Individu with another island. Island model GA could create university course timetabling in reasonable time. This distributed model could run faster rather than single machine model decreasing constr aint violations to reach optimum fitness. It could have less constraint violations because it could escape from stagnant local optimum easier. Island model GA could even produced great accuracy for Telkom University dataset ( 99.74% ) and acceptable accuracy at 96.80% for Purdue dataset for student level timetabling .Keyword: timetabling, university course timetabling problem, informed genetic algorithm, island model genetic algorith