A comparison of annealing techniques for academic course scheduling

Elmohamed, M. A. Saleh; Coddington, Paul; Fox, Geoffrey

doi:10.1007/bfb0055883

Cited by 73 publications

(41 citation statements)

References 24 publications

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“…Consequently, T reheat will take a value that is only a very small way above T pt . In both [10] and [51] this method of reheating is reported to be very effective at producing good timetables; indeed, as we will see in our own SA algorithm documented in Chapter 6 later, it is also appropriate for our needs as well. showing how we can use this information to calculate T pt .…”

Section: One Stage Optimisation Algorithmsmentioning

confidence: 89%

“…Abramson [9], Melicio et al [79], and Elmohamed et al [51] have also reported one-stage optimisation algorithms that make use of the simulated annealing (SA) metaheuristic. In the approach of Elmohamed et al, for example, the authors consider the timetabling problem of Syracuse University in the USA and use a weighted-sum scoring function that heavily penalises violations of the hard constraints.…”

Section: One Stage Optimisation Algorithmsmentioning

confidence: 99%

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Metaheuristics for University Course Timetabling

Lewis

Paechter

Rossi-Doria

2007

Evolutionary Scheduling

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The work presented in this thesis concerns the problem of timetabling at universitiesparticularly course-timetabling, and examines the various ways in which metaheuristic techniques might be applied to these sorts of problems. Using a popular benchmark version of a university course timetabling problem, we examine the implications of using a "twostaged" algorithmic approach, whereby in stage-one only the mandatory constraints are considered for satisfaction, with stage-two then being concerned with satisfying the remaining constraints but without re-breaking any of the mandatory constraints in the process.Consequently, algorithms for each stage of this approach are proposed and analysed in detail.For the first stage we examine the applicability of the so-called Grouping Genetic Algorithm (GGA). In our analysis of this algorithm we discover a number of scaling-up issues surrounding the general GGA approach and discuss various reasons as to why this is so. Two separate ways of enhancing general performance are also explored. Secondly, an Iterated Heuristic Search algorithm is also proposed for the same problem, and in experiments it is shown to outperform the GGA in almost all cases. Similar observations to these are also witnessed in a second set of experiments, where the analogous problem of colouring equipartite graphs is also considered.Two new metaheuristic algorithms are also proposed for the second stage of the twostaged approach: an evolutionary algorithm (with a number of new specialised evolutionary operators), and a simulated annealing-based approach. Detailed analyses of both algorithms are presented and reasons for their relative benefits and drawbacks are discussed.Finally, suggestions are also made as to how our best performing algorithms might be modified in order to deal with further "real-world" constraints. In our analyses of these modified algorithms, as well as witnessing promising behaviour in some cases, we are also able to highlight some of the limitations of the two-stage approach in certain cases.ii Table of Fig. 3.2:A demonstration of the matrix representation for timetables used throughout this thesis. Here, event 11 has been assigned to room 2 and timeslot 2, event 8 has been assigned to room 2, timeslot 11, and so on. Also indicated in this diagram is the presence of the end-of-day timeslots (which will occur in timeslots 9, 18, 27, 36 and 45). These might be considered slightly different to the remaining forty timeslots, because events that are assigned to these will automatically cause soft constraint SC to have a higher distance-to-feasibility than the best candidate solution in the initial population of the GGA, and will thus generally start at a higher point on the y-axis. 84 1: IntroductionTimetables are ubiquitous in many areas of daily life such as work, education, transport, and entertainment. Indeed, it is quite difficult to imagine an organized and modern society coping without them. Yet in many real-world cases, particularly where resources (such as peopl...

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Section: One Stage Optimisation Algorithmsmentioning

confidence: 89%

Section: One Stage Optimisation Algorithmsmentioning

confidence: 99%

Metaheuristics for University Course Timetabling

Lewis

Paechter

Rossi-Doria

2007

Evolutionary Scheduling

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“…Unfortunately, SA also has many well-documented disadvantages. It requires extensive computational work [1,15,34,53]. Furthermore, SA is sensitive to the choice of its many parameters which can be difficult to fine tune [1,15,38,41,47,53].…”

Section: Simulated Annealingmentioning

confidence: 99%

“…It requires extensive computational work [1,15,34,53]. Furthermore, SA is sensitive to the choice of its many parameters which can be difficult to fine tune [1,15,38,41,47,53]. For example, there are at least a dozen different temperature cooling schedule from which to choose [18,26,44,53].…”

Section: Simulated Annealingmentioning

confidence: 99%

Optimizing an emperical scoring function for transmembrane protein structure determination.

Young¹,

Sale²,

Gray³

et al. 2003

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We examine the problem of transmembrane protein structure determination. Like many other questions that arise in biological research, this problem cannot be addressed by traditional laboratory experimentation alone. An approach that integrates experiment and computation is required. We investigate a procedure which states the transmembrane protein structure determination problem as a bound constrained optimization problem using a special empirical scoring function, called Bundler, as the objective function. In this paper, we describe the optimization problem and some of its mathematical properties. We compare and contrast results obtained using two different derivative free optimization algorithms.

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“…Since then, the problem has been tackled with various approaches including graph coloring [23], network flows [6] and operations research methods [1]. During the last years, various artificial intelligence techniques have also been used against the problem, like tabu search [3], [30], simulated annealing [9], [27], genetic algorithms [24], [10] and constraint programming [16], [22], [19], [11], [26].…”

Section: Introductionmentioning

confidence: 99%

A Generic Object-Oriented Constraint-Based Model for University Course Timetabling

Zervoudakis¹,

Stamatopoulos²

2001

Lecture Notes in Computer Science

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Abstract. The construction of course timetables for academic institutions is a very difficult problem with a lot of constraints that have to be respected and a huge search space to be explored, even if the size of the problem input is not significantly large, due to the exponential number of the possible feasible timetables. On the other hand, the problem itself does not have a widely approved definition, since different variations of it are faced by different departments. However, there exists a set of entities and constraints among them which are common to every possible instantiation of the timetabling problem. In this paper, we present a model of this common core in terms of Ilog Solver, a constraint programming object-oriented C++ library, and we show the way this model may be extended to cover the needs of a specific academic unit.

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A comparison of annealing techniques for academic course scheduling

Cited by 73 publications

References 24 publications

Metaheuristics for University Course Timetabling

Metaheuristics for University Course Timetabling

Optimizing an emperical scoring function for transmembrane protein structure determination.

A Generic Object-Oriented Constraint-Based Model for University Course Timetabling

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