Efficient solutions for a university timetabling problem through integer programming

A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems

General Issues.- A Perspective on Bridging the Gap Between Theory and Practice in University Timetabling.- Very Large-Scale Neighborhood Search Techniques in Timetabling Problems.- Measurability and Reproducibility in University Timetabling Research: Discussion and Proposals.- Employee Timetabling.- Physician Scheduling in Emergency Rooms.- A Flexible Model and a Hybrid Exact Method for Integrated Employee Timetabling and Production Scheduling.- Memes, Self-generation and Nurse Rostering.- An Evaluation of Certain Heuristic Optimization Algorithms in Scheduling Medical Doctors and Medical Students.- Timetabling of Meetings.- Scheduling Research Grant Proposal Evaluation Meetings and the Range Colouring Problem.- Sports Timetabling.- Constructive Algorithms for the Constant Distance Traveling Tournament Problem.- Scheduling the Brazilian Soccer Tournament with Fairness and Broadcast Objectives.- Referee Assignment in Sports Leagues.- A Branch-and-Cut Algorithm for Scheduling the Highly-Constrained Chilean Soccer Tournament.- Course Timetabling.- Modeling and Solution of a Complex University Course Timetabling Problem.- Timetabling Problems at the TU Eindhoven.- The Teaching Space Allocation Problem with Splitting.- Solving the University Timetabling Problem with Optimized Enrollment of Students by a Self-adaptive Genetic Algorithm.- School Timetabling.- A Case Study for Timetabling in a Dutch Secondary School.- Scheduling School Meetings.- Hierarchical Timetable Construction.- The KTS High School Timetabling System.- Examination Timetabling.- A Novel Fuzzy Approach to Evaluate the Quality of Examination Timetabling.- Linear Linkage Encoding in Grouping Problems: Applications on Graph Coloring and Timetabling.- Ant Algorithms for the Exam Timetabling Problem.- An Extensible Modelling Framework for Timetabling Problems.- An Experimental Study on Hyper-heuristics and Exam Timetabling.

Practice and Theory of Automated Timetabling VI

A common weakness of local search metaheuristics, such as Simulated Annealing, in solving combinatorial optimisation problems, is the necessity of setting a certain number of parameters. This tends to generate a significant increase in the total amount of time required to solve the problem and often requires a high level of experience from the user. This paper is motivated by the goal of overcoming this drawback by employing "parameter-free" techniques in the context of automatically solving course timetabling problems.
We employ local search techniques with "straightforward" parameters, i.e. ones 
that an inexperienced user can easily understand. In particular, we present an 
extended variant of the "Great Deluge" algorithm, which requires only two 
parameters (which can be interpreted as search time and an estimation of the 
required level of solution quality). These parameters affect the performance 
of the algorithm so that a longer search provides a better result - as long as 
we can intelligently stop the approach from converging too early. Hence, a user 
can choose a balance between processing time and the quality of the solution. 
The proposed method has been tested on a range of university course timetabling 
problems and the results were evaluated within an International Timetabling 
Competition. The effectiveness of the proposed technique has been confirmed 
by a high level of quality of results. These results represented the third 
overall average rating among 21 participants and the best solutions on 8 of 
the 23 test problems.

https://scindeks-clanci.ceon.rs/data/pdf/0354-0243/2003/0354-02430302139B.pdf

A time-predefined approach to course timetabling

Practice and theory of automated timetabling VI : 6th international conference, PATAT 2006, Brno, Czech Republic, August 30-September 1, 2006 : revised selected papers

We present a two-phase genetic algorithm (TGA) to solve timetabling problems for universities. Here, we use two kinds of populations. The first population is related to class scheduling, and the second one is related to room allocation. These populations are evolved independently, and a cost value of each individual is calculated. Then several individuals with the lowest costs are selected from each population, and these individuals are combined in order to calculate the fitness values. To evaluate the performance of TGA, we apply TGA to several timetabling problems and compare results obtained by TGA with those obtained by the simple GA (SGA). For the timetabling problem based on the curriculum of the Faculty of Information Sciences at Hiroshima City University, TGA finds a solution that satisfies all constraints, but SGA cannot find a feasible solution. From the results for problems generated by an automatic timetabling problem generator, we show that TGA obtains a better solution than the simple GA when the room utilization ratio is high.

A Co-evolving Timeslot/Room Assignment Genetic Algorithm Technique for University Timetabling

We compare genetic algorithms (GA) that solve timetabling problems for universities. Here, we define a timetabling problem as that of finding both a class schedule and room allocation that do not conflict with any constraints. We have already proposed the TGA, which evolves two kinds of genotypes, one related to class scheduling, the other related to room allocation. However, TGA has not been applied to many kinds of problems and it has not been compared with other methods. Here, we present four kinds of genetic algorithms, TGA, STGA, SGA, and SGA2. TGA and STGA use the two kinds of genotypes mentioned above. TGA evolves these genotypes alternately, while STGA evolves those sequentially. SGA and SGA2 are based on the simple GA that uses one genotype. SGA evolves a population to find feasible class scheduling and room allocation, while SGA2 evolves a population to find only a feasible class schedule for which feasible room allocation is possible. These algorithms are implemented and we apply these methods to several timetabling problems generated by an automatic timetabling problem generator. We also implement two methods, SA and TS; experiments by those methods have been performed, where SA is a method based on simulated annealing and TS is a method based on Tabu search. The experimental results show that SGA2 finds feasible solutions for almost all problems, and TGA finds feasible solutions when the average number of subjects per class is high. In addition, STGA finds a feasible solution when the problem has many classes, and SA finds feasible solutions when the number of classes is small and it has many constraints and requests. 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(7): 1–12, 2004; Published online in Wiley InterScience (). DOI 10.1002sscj.10682 © 2004 Wiley Periodicals, Inc.

Comparisons of genetic algorithms for timetabling problems

We present a two-phase genetic algorithm (TGA) to solve timetabling problems for universities. 1 Here, we use two kinds of populations. The first population is related to class scheduling, and the second one is related to room allocation. These populations are evolved independently, and a cost value of each individual is calculated. Then several individuals with the lowest costs are selected from each population, and these individuals are combined in order to calculate the fitness values. To evaluate the performance of TGA, we apply TGA to several timetabling problems and compare results obtained by TGA with those obtained by the simple GA (SGA). For the timetabling problem based on the curriculum of the Faculty of Information Sciences at Hiroshima City University, TGA finds a solution that satisfies all constraints, but SGA cannot find a feasible solution. From the results for problems generated by an automatic timetabling problem generator, we show that TGA obtains a better solution than the simple GA when the room utilization ratio is high.

A co-evolving timeslot/room assignment genetic algorithm technique for university timetabling

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A Co-evolving Timeslot/Room Assignment Genetic Algorithm Technique for University Timetabling

Comparisons of genetic algorithms for timetabling problems

A co-evolving timeslot/room assignment genetic algorithm technique for university timetabling