The University of Edinburgh -
Division of Informatics
Forrest Hill & 80 South Bridge

PhD Thesis #9410

Title:Genetic Algorithms in Timetabling and Scheduling
Date: 1994
Abstract:This thesis investigates the use of genetic algorithms (GAs) for solving a range of timetabling and scheduling problems. Such problems are very hard in general, and GAs offer a useful and successful alternative to existing techniques. A framework is presented for GAs to solve modular timetabling problems in educational institutions. The approach involves three components: declaring problem-specific constraints, constructing a problem-specific evaluation function and using a problem-independent GA to attempt to solve the problem. Successful results are demonstrated and a general analysis of the reliability and robustness of the approach is conducted. The basic approach can readily handle a wide variety of general timetabling problem constraints, and is therefore likely to be of great practical usefulness (indeed, an earlier version is already in use). The approach relies for its success on the use of specially designed mutation operators which greatly improve upon the performance of a GA with standard operators. A framework for GAs in job-shop and open-shop scheduling is also presented. One of the key aspects of this approach is the use of specially designed representations for such scheduling problems. The representations implicitly encode a schedule by encoding instructions for a schedule builder. The general robustness of this approach is demonstrated with respect to experiments on a range of widely-used benchmark problems involving many different schedule quality criteria. When compared against a variety of common heuristic search approaches, the GA approach is clearly the most successful method overall. An extension to the representation, in which choices of heuristic for the schedule builder are also incorporated in the chromosome, is found to lead to new best results on the makespan for some well known benchmark open-shop scheduling problems. The general approach is also shown to be readily extendible to rescheduling and dynamic schedulin

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