Cant sawing log positioning in a sawmill: Searching for an optimal solution

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dc.contributor.authorWessels C.B.
dc.contributor.authorde V du Plessis J.
dc.contributor.authorSmit N.H.
dc.date.accessioned2011-10-13T16:58:26Z
dc.date.available2011-10-13T16:58:26Z
dc.date.issued2011
dc.description.abstractAutomated three-dimensional log scanning and positioning systems in a sawmill have a limited time available to reach an optimal positioning solution before primary breakdown sawing starts. In this study a search algorithm (tentacle algorithm) was developed for this task and was empirically evaluated in terms of its ability to find an optimal or close-to-optimal positioning solution in a limited number of iterations. This algorithm was compared to the population-based incremental learning algorithm, the simulated annealing algorithm, and the particle swarm optimisation algorithm. The tentacle algorithm performed the best of all the algorithms evaluated in terms of the mean volume recovery obtained. However, exhaustive searches around the centred and 'horns-up' and 'horns-down' positions using smaller ranges resulted in better mean volume recovery results than any of the algorithms, although the mean results using this strategy did not differ significantly from that of the tentacle algorithm. © NISC (Pty) Ltd.
dc.description.versionArticle
dc.identifier.citationSouthern Forests
dc.identifier.citation73
dc.identifier.citation1
dc.identifier.citationhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960674892&partnerID=40&md5=998513210216e0f5455ffad9a6c99bb3
dc.identifier.issn20702620
dc.identifier.other10.2989/20702620.2011.576486
dc.identifier.urihttp://hdl.handle.net/10019.1/16722
dc.subjectLog positioning
dc.subjectMetaheuristics
dc.subjectSawmill
dc.titleCant sawing log positioning in a sawmill: Searching for an optimal solution
dc.typeArticle
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