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Determining optimal primary sawing and ripping machine settings in the wood manufacturing chain

dc.contributor.advisorVlok, P. J.en_ZA
dc.contributor.advisorWessels, C. B.en_ZA
dc.contributor.authorLindner, Berndt Geralden_ZA
dc.contributor.otherStellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.en_ZA
dc.date.accessioned2014-04-16T17:31:32Z
dc.date.available2014-04-16T17:31:32Z
dc.date.issued2014-04en_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/86672
dc.descriptionThesis (MEng)--Stellenbosch University, 2014.en_ZA
dc.description.abstractENGLISH ABSTRACT: For wood manufacturers around the world, the single biggest cost factor is known to be its raw material. Thus maximum utilisation, specifically volume recovery of this raw material, is of key importance for the industry. The wood products industry consists of several interrelated manufacturing steps for converting trees into logs and logs into finished lumber. At most primary and secondary wood processors the different manufacturing steps are optimised in isolation or based on operator experience. This can lead to suboptimal decisions and a substantial waste of raw material. The objective of this study was to determine the optimal machine settings for two interrelated operations, namely the sawing and ripping operations which have traditionally been optimised individually. A model, having two decision variables, was developed which aims to satisfy market demand at a minimal cost. The first decision was how to saw the log supply into different thicknesses by choosing specific sawing patterns. The second was to decide on a rip saw’s settings, namely part priority values, which determines how the products from the primary sawing operation are ripped into products of a certain thickness and width. The techniques used to determine the machine settings included static simulation with the SIMSAW software to represent the sawing operation and mixed integer programming to model the ripping operation. A metaheuristic, namely the Population Based Incremental Learning algorithm, was the link between the two operations and determined the optimal settings for the combined process. The model’s objective function was formulated to minimise the cost of production. This cost included the raw material waste cost and the over or under production cost. The over production cost was estimated to include the stock keeping costs. The under production cost was estimated as the buy-in cost of purchasing the under supplied products from another wood supplier. The model performed well against current decision software available in South Africa, namely the Sawmill Production Planning System package, which combines simulation (SIMSAW) and mixed integer programming techniques to maximise profit. The model added further value in modelling and determining the ripping priority settings in addition to the primary sawing patterns.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Die grootste enkele koste vir houtprodukvervaardigers wêreldwyd is dié van hulle roumateriaal. Die maksimale gebruik van rou materiaal, of volume herwinning, is dus van primêre belang vir hierdie industrie. Die vervaardigingsproses in die houtprodukte-industrie bestaan uit ‘n verskeidenheid interafhanklike stappe om bome na stompe te verwerk en stompe na eindprodukte. By meeste primêre -en sekondêre houtvervaardigers word die verskillende vervaardigingsstappe in isolasie ge-optimeer. Hierdie praktyk lei tot sub-optimale besluite en ‘n vermorsing van roumateriale. Die doelwit van hierdie studie was om die optimale masjienverstellings vir twee interafhanklike prosesse, die primêre -en kloofsaag prosesse, te bepaal. Tradisioneel word hierdie twee prosesse individueel optimeer. ‘n Model met twee besluitnemingsveranderlikes is ontwikkel wat poog om die markaanvraag te bevredig teen ‘n minimum koste. Die eerste besluit was watter saagpatroon gekies moet word om die stompe in die regte dikte produkte te saag. Die tweede besluit was wat die kloofsaagstellings, ook bekend as prioriteitswaardes, moet wees sodat die regte wydte produkte gesaag word. Die tegnieke wat gebruik is sluit statiese simulasie met SIMSAW sagteware in om die primêre saagproses te modelleer en gemengde heelgetalprogammering (“mixed integer programming”) om die kloofsaagproses te modelleer. ‘n Metaheuristiek genaamd die “Population Based Incremental Learning” algoritme,was die skakel tussen die twee operasies om die optimale masjienstellings vir die proses te bepaal. Die model se doelfunksie was geformuleer om die koste van produksie te minimeer. Hierdie koste sluit die roumateriaal afvalkoste en die kostes van oor -en onderproduksie in. Die oorproduksiekoste was ‘n skatting van die voorraadkostes. Die onderproduksiekoste was ‘n skatting van die koste om voorraad van ‘n ander verskaffer aan te koop. Die model het goed opgeweeg teen die beskikbare besluitnemingssagteware in Suid Afrika, die “Sawmill Production Planning System”, wat ‘n kombinasie van SIMSAW en ‘n gemengde heelgetalprogrammeringstegniek is. Die model het verder waarde toegevoeg deur die kloofsaag se prioriteitswaardes te modelleer saam met die primêre saagpatrone.af_ZA
dc.format.extentxv, 164 p. : ill.
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.subjectSawmills -- Cost effectivenessen_ZA
dc.subjectForest and wood scienceen_ZA
dc.subjectMetaheuristicsen_ZA
dc.subjectWood -- Processingen_ZA
dc.subjectSawmills -- Data processingen
dc.subjectDissertations -- Industrial engineeringen_ZA
dc.subjectUCTD
dc.subjectCosts, Industrialen_ZA
dc.subject.otherTheses -- Industrial engineeringen_ZA
dc.titleDetermining optimal primary sawing and ripping machine settings in the wood manufacturing chainen_ZA
dc.typeThesisen_ZA
dc.rights.holderStellenbosch Universityen_ZA


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