An effective genetic algorithm for job shop scheduling

Wang, W. and Brunn, P. (2000) An effective genetic algorithm for job shop scheduling. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 214 (4). pp. 293-300. ISSN 0954-4054

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Abstract

This paper presents an effective genetic algorithm (GA) for job shop sequencing and scheduling. A simple and universal gene encoding scheme for both single machine and multiple machine models and their corresponding genetic operators, selection, sequence-extracting crossover and neighbour-swap mutation are described in detail. A simple heuristic rule is adapted and embedded into the GA to avoid the production of unfeasible solutions. The results of computing experiments for a number of scheduling problems have demonstrated that the GA described in the paper is effective and efficient in terms of the quality of solution and the computing cost.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences

University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
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Depositing User: Vishal Gautam
Date Deposited: 26 Aug 2011 15:26
Last Modified: 21 Apr 2020 21:12
URI: https://ueaeprints.uea.ac.uk/id/eprint/22745
DOI: 10.1243/0954405001517685

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