Results 1 
5 of
5
A Survey of Automated Timetabling
 ARTIFICIAL INTELLIGENCE REVIEW
, 1999
"... The timetabling problem consists in fixing a sequence of meetings between teachers and students in a prefixed period of time (typically a week), satisfying a set of constraints of various types. A large number of variants of the timetabling problem have been proposed in the literature, which diff ..."
Abstract

Cited by 145 (14 self)
 Add to MetaCart
The timetabling problem consists in fixing a sequence of meetings between teachers and students in a prefixed period of time (typically a week), satisfying a set of constraints of various types. A large number of variants of the timetabling problem have been proposed in the literature, which differ from each other based on the type of institution involved (university or high school) and the type of constraints. This problem, that has been traditionally considered in the operational research field, has recently been tackled with techniques belonging also to artificial intelligence (e.g. genetic algorithms, tabu search, simulated annealing, and constraint satisfaction). In this paper, we survey the various formulations of the problem, and the techniques and algorithms used for its solution.
Constructing School Timetables using Simulated Annealing: Sequential and Parallel Algorithms
, 1991
"... : This paper considers a solution to the school timetabling problem. The timetabling problem involves scheduling a number of tuples, each consisting of class of students, a teacher, a subject and a room, to a fixed number of time slots. A Monte Carlo scheme called simulated annealing is used as an o ..."
Abstract

Cited by 70 (4 self)
 Add to MetaCart
: This paper considers a solution to the school timetabling problem. The timetabling problem involves scheduling a number of tuples, each consisting of class of students, a teacher, a subject and a room, to a fixed number of time slots. A Monte Carlo scheme called simulated annealing is used as an optimisation technique. The paper introduces the timetabling problem, and then describes the simulated annealing method. Annealing is then applied to the timetabling problem. A prototype timetabling environment is described followed by some experimental results. A parallel algorithm which can be implemented on a multiprocessor is presented. This algorithm can provide a faster solution than the equivalent sequential algorithm. Some further experimental results are given. 1 INTRODUCTION This paper considers a solution to the school timetabling problem. The timetabling problem involves scheduling a number of tuples, each consisting of class of students, a teacher, a subject and a room, to a fixe...
Solving Timetables using Simulated Annealing Page 1 Constructing School Timetables using Simulated Annealing: Sequential and Parallel Algorithms
"... This paper considers a solution to the school timetabling problem. The timetabling problem involves scheduling a number of tuples, each consisting of class of students, a teacher, a subject and a room, to a fixed number of time slots. A Monte Carlo scheme called simulated annealing is used as an opt ..."
Abstract
 Add to MetaCart
This paper considers a solution to the school timetabling problem. The timetabling problem involves scheduling a number of tuples, each consisting of class of students, a teacher, a subject and a room, to a fixed number of time slots. A Monte Carlo scheme called simulated annealing is used as an optimisation technique. The paper introduces the timetabling problem, and then describes the simulated annealing method. Annealing is then applied to the timetabling problem. A prototype timetabling environment is described followed by some experimental results. A parallel algorithm which can be implemented on a multiprocessor is presented. This algorithm can provide a faster solution than the equivalent sequential algorithm. Some further experimental results are given. 1
Heuristics
"... Abstract. The timetabling problem consists in scheduling a sequence of lectures between teachers and students in a prefixed period of time (typically a week), satisfying a set of constraints of various types. A large number of variants of the timetabling problem have been proposed in the literature, ..."
Abstract
 Add to MetaCart
Abstract. The timetabling problem consists in scheduling a sequence of lectures between teachers and students in a prefixed period of time (typically a week), satisfying a set of constraints of various types. A large number of variants of the timetabling problem have been proposed in the literature, which differ from each other based on the type of institution involved (university or school) and the type of constraints. This problem, that has been traditionally considered in the operational research field, has recently been tackled with techniques belonging also to Artificial Intelligence (e.g., genetic algorithms, tabu search, and constraint satisfaction). In this paper, we survey the various formulations of the problem, and the techniques and algorithms used for its solution. 1.
Finding Feasible Timetables using GroupBased Operators
"... Abstract — This paper describes the applicability of the socalled ‘grouping genetic algorithm ’ to a wellknown version of the university course timetabling problem. We note that there are, in fact, various scaling up issues surrounding this sort of algorithm and, in particular, see that it behaves ..."
Abstract
 Add to MetaCart
Abstract — This paper describes the applicability of the socalled ‘grouping genetic algorithm ’ to a wellknown version of the university course timetabling problem. We note that there are, in fact, various scaling up issues surrounding this sort of algorithm and, in particular, see that it behaves in quite different ways with different sized problem instances. As a byproduct of these investigations, we introduce a method for measuring population diversities and distances between individuals with the grouping representation. We also look at how such an algorithm might be improved: firstly, through the introduction of a number of different fitness functions and, secondly, through the use of an additional stochastic localsearch operator (making in effect a grouping memetic algorithm). In many cases, we notice that the best results are actually returned when the grouping genetic operators are removed altogether, thus highlighting many of the issues that are raised in the study. Index Terms—Diversity, fitnessfunctions, groupingproblems, timetabling.