Martin Caron Comparaison de l`optimisation selon le prix et selon le

Transcription

Martin Caron
Comparaison de l’optimisation selon le prix
et selon le rendement matière dans les usines
de débitage de composants de bois franc
Mémoire
présenté
à la Faculté des études supérieures
pour l’obtention
du grade de maître ès sciences (M.Sc.)
Département des sciences du bois et de la forêt
FACULTÉ DE FORESTERIE ET GÉOMATIQUE
UNIVERSITÉ LAVAL
AVRIL 2003
© Martin Caron, 2003
Résumé
La recherche sur les procédés et les indicateurs de performance de l’industrie des composants
de bois franc est souvent réalisée sur la base du rendement matière. Plusieurs études ont été
effectuées à l’aide de logiciels informatiques afin de simuler les procédés de débitage et de
trouver des stratégies et techniques pour améliorer le rendement matière. La présente étude a
été réalisée afin de comparer l’optimisation selon la valeur des pièces produites versus
l’optimisation selon le rendement matière, à l’aide d’une structure formelle de prix inspirée
du système de classement du bois franc de la National Hardwood Lumber Association.
Les résultats de cette étude démontrent clairement qu’une optimisation selon le rendement
matière est sous-optimale en termes de la valeur des pièces produites par rapport à une
optimisation selon la valeur et n’apporte pas le retour monétaire maximum par rapport au
bois utilisé. Pour de l’érable à sucre de grade No. 1 Commun avec un procédé de délignage
en tête, changer d’une optimisation selon la surface à une optimisation selon la valeur
produite des pièces a résulté en une diminution relative de rendement de 10.6% et en une
augmentation relative du retour monétaire apporté entre la valeur produite et le coût de bois,
de 107.6%.
© Martin Caron
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Remerciements
Merci au Centre de Recherche Industrielle du Québec (CRIQ) pour m’avoir ouvert les
horizons dans le domaine du bois autant par le développement que par l’utilisation du logiciel
« Optimiseur deux axes ».
Merci à Michel R. Bouchard pour son support éclairé lors de la rédaction de ce mémoire et sa
précieuse aide dans le développement des outils d’analyse sur « Matlab ».
Merci à Robert Beauregard pour ses contacts, son support au cours de la rédaction de ce
mémoire et sa connaissance éclairée du domaine du bois.
Je souhaite au lecteur autant de plaisir et d’émerveillement à lire et comprendre ce mémoire
que j’en ai eu à l’écrire.
© Martin Caron
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Avant-propos
Le mémoire est constitué d’une introduction et d’une conclusion générales en français ainsi
que d’un article intercalé en anglais entre les deux autres sections. L’article intitulé
“Comparing a value based and a surface based part prioritization in the hardwood rough
mill” a été soumis pour publication dans le Forest Products Journal. Les auteurs de l’article
sont Martin Caron, Michel R. Bouchard et Robert Beauregard. Le premier auteur a réalisé
les simulations, les analyses statistiques et il a rédigé le manuscrit sous la direction des deux
co-auteurs, respectivement du CRIQ et de l’Université Laval.
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Table des matières
Résumé ................................................................................................................ ii
Remerciements .................................................................................................. iii
Avant-propos ..................................................................................................... iv
Table des matières ..............................................................................................v
Liste des tableaux.............................................................................................. vi
Liste des figures................................................................................................ vii
1. Introduction..................................................................................................1
2. Comparing a value based and a surface based part prioritization in the
hardwood rough mill. .........................................................................................6
2.1.
Introduction .............................................................................................................6
2.2.
Materials and methods..........................................................................................11
2.2.1.
Definitions .......................................................................................................11
2.2.2.
Board database description.............................................................................12
2.2.3.
Part grade definition .......................................................................................15
2.2.4.
Defects definition.............................................................................................15
2.2.5.
Part grades in relation with defects ................................................................16
2.2.6.
Cutting bill.......................................................................................................16
2.2.7.
The pricing structure .......................................................................................17
2.2.8.
NHLA dimension definitions............................................................................17
2.2.9.
Value in the pricing structure depend on parts dimensions ............................18
2.2.10. Simulation process...........................................................................................20
2.2.11. Statistical analysis ...........................................................................................20
2.3.
Results and discussion ...........................................................................................22
2.3.1.
Effect of the processes on yield........................................................................24
2.3.2.
Effect of the process on RPBF.........................................................................26
2.3.3.
Effect of the prioritization mechanism on yield...............................................29
2.3.4.
Effect of the prioritization mechanism on RPBF.............................................30
2.4.
Conclusions.............................................................................................................33
2.5.
Litterature cited .....................................................................................................36
3. Conclusion...................................................................................................51
4. Liste des références ....................................................................................54
Annexe 1 : Prix calculés....................................................................................57
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Liste des tableaux
TABLE 1. — DESCRIPTION OF THE HARD MAPLE AND BLACK CHERRY BOARD DATABASE ........40
TABLE 2. — PART GRADES DESCRIPTION ................................................................................40
TABLE 3. — DESCRIPTIONS OF DEFECTS IN THE BOARD DATABASE ..........................................41
TABLE 4 . — DEFINITION OF PART GRADES IN RELATIONS WITH DEFECTS ...............................41
TABLE 5. —CUTTING BILL USED IN THE SIMULATIONS ............................................................42
TABLE 6. — CUTTING DIMENSIONS IN THE NHLA GRADING SYSTEM ......................................43
TABLE 7. — PART DIMENSION LIMITS USED FOR THE PRICING STRUCTURE OF THE CUTTING BILL
.........................................................................................................................................43
TABLE 8. — HARD MAPLE PRICING, AND WIDTH/LENGTH FACTORS .........................................43
TABLE 9. — PRODUCT / GRADES PRICING AND PRICE FACTOR ..................................................44
TABLE 10. —OPTIMISATION PARAMETERS ...............................................................................45
TABLE 11. — OPTIMIZATION RESULTS FOR THE HARD MAPLE DATABASE ................................46
TABLE 12. — OPTIMIZATION RESULTS FOR THE BLACK CHERRY DATABASE ............................47
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Liste des figures
FIGURE 1. —BOREALSCAN IMAGE ACQUISITION SYSTEM ..........................................................48
FIGURE 2. ― PRODUCTS DISTRIBUTION FOR HARD MAPLE .......................................................49
FIGURE 3. ― PRODUCTS DISTRIBUTION FOR BLACK CHERRY ...................................................50
© Martin Caron
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1. Introduction
La base d’analyse de la rentabilité pour l’industrie du bois franc est souvent le rendement
matière. Face aux pressions du marché, les entreprises spécialisées dans le débitage du bois
franc ont mis l’accent sur l’augmentation de leur rendement matière (Buehlmann 1998).
Déjà au début des années 1980, la loi de l’offre et de la demande combinée avec le déclin de
la ressource naturelle avaient provoqué une hausse drastique des prix (Hall et al. 1980).
Cette tendance s’est poursuivie au cours des années 1990 et 2000. Au Québec, une partie
grandissante des billes utilisées au sciage provient maintenant d’importations à cause de la
piètre qualité et de la rareté des billes locales (Industrie Canada 2002). Forcée par
l’augmentation des coûts de main d’oeuvre en Amérique du nord, l’augmentation du prix de
la matière première et la mondialisation des marchés, l’industrie doit trouver de nouvelles
façons de faire pour maintenir sa position concurrentielle (El Radi et al. 1994, Hoff et al.
1997). Pour les industriels du domaine du bois, l’optimisation des procédés et le
développement de nouveaux produits sont devenus des moyens nécessaires pour assurer leur
survie.
L’industrie du débitage du bois franc demeure loin derrière les autres industries en ce qui a
trait à l’innovation technologique (Buehlmann 1998). Les sources potentielles
d’amélioration de la compétitivité doivent être analysées. L’industrie doit se différencier,
trouver des moyens de diminuer les coûts, d’augmenter sa productivité et mettre au point de
nouvelles façons de faire. Il est nécessaire de voir plus loin, de viser un accroissement de la
rentabilité par la recherche sur le procédé (Hoff et al. 1997). La recherche d’aujourd’hui se
base sur des logiciels de simulations de débitage (Thomas 1996b, 1997), des systèmes de
vision (Conners et al. 1997), des traitements de la matière première en procédant à une présélection ou une amélioration des planches afin d’obtenir un gain de rendement matière
(Gatchell 1990, Gatchell et Thomas 1997, 1998, Wiedenbeck et Scheerer 1996, Wiedenbeck
2001, Steele et Wiedenbeck 1999). L’optimisation du procédé de débitage, considérant la
valeur des pièces produites ainsi que leur contribution en rendement a été suggérée dans des
études antérieures (Thomas 1996b, Mitchell 2002). Une méthode formelle permettant de
générer une structure de prix et les effets sur le procédé que provoque une optimisation selon
le prix, n’ont jamais été étudiés directement par simulation.
Le procédé conventionnel de débitage du bois franc a longtemps été considéré comme celui
procédant par le tronçonnage en tête (Thomas 1997, Buehlmann 1998). Le tronçonnage en
tête consiste à faire des coupes sur les planches selon la largeur de celles-ci. La rareté des
billes de fortes dimensions, les prix de la matière première en hausse, les besoins changeants
de l’industrie en fonction des besoins des clients et de la complexité des paniers de produits,
ainsi que des cadences de production plus élevées ont partiellement et graduellement poussés
les producteurs de composants vers un procédé de délignage en tête (Mullin 1990,
Wiedenbeck et Scheerer 1996, Wiedenbeck 2001). Le délignage en tête consiste à faire des
coupes sur les planches selon la longueur de celles-ci. Il faut noter cependant que chacun de
ces deux procédés de base possède ses avantages et offre un potentiel supérieur dans
certaines conditions.
En usine, les planches sont transformées en bandes (délignés ou tronçons) afin d’optimiser le
rendement en surface sur le premier axe traité (Thomas 1996a). Ces bandes sont ensuite
traitées pour en purger les défauts et respecter les dimensions spécifiées par le carnet de
commande, tout en tentant de maximiser le rendement matière. Il a été mentionné que le
délignage en tête tend à produire des pièces plus longues que le tronçonnage en tête et offre
généralement un meilleur rendement dans les grades intermédiaires (No. 1 commun et No.
2A commun). Le tronçonnage en tête permet de purger des zones de défauts concentrés sur
une courte longueur et offre généralement un meilleur rendement dans les grades inférieurs
(No. 3 commun) (Wiedenbeck 2001). Les procédés de « délignage toutes longueurs »
(plancher, moulures) et « tronçonnage toutes largeurs » (panneaux) sont des extensions des
procédés de délignage en tête et de tronçonnage en tête offrant des avantages du fait qu’ils
possèdent un axe de liberté sur une dimension pour le placement des pièces.
© Martin Caron
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La recherche sur les procédés de débitage est effectuée avec différentes techniques, l’une
d’elles étant la simulation par logiciels informatiques. Ces outils de simulation utilisent des
bases de données de planches numérisées, un carnet de commande contenant les informations
sur les pièces à produire et des paramètres représentant le procédé. Une base de données
consiste en un ensemble de planches classifiées selon certaines règles ou critères, dont les
dimensions sont connues et dont les défauts sont identifiés et localisés. Un carnet de
commande consiste en une liste de pièces à produire dans une usine de débitage . Les
dimensions, grades, quantités et prix y sont contenus pour chaque pièce (Buehlmann 1998).
Un travail de comparaison entre l’Optimiseur deux axes (Caron 2001) et les logiciels du
USDA ROMI-RIP et ROMI-CROSS (Thomas 1996a, 1997) pour du délignage en tête et du
tronçonnage en tête a été effectué et permet d’établir un niveau de performance relatif entre
ces outils (Bouffard et al. 2002). L’étude se base sur les résultats de simulations produits par
l’Optimiseur deux axes du système BorealScan développé par le CRIQ. La base de données
est constituée de bois classifié selon les règles de la National Hardwood Lumber Association
(NHLA 1998) et contient plus de 2700 planches numérisées d’érable à sucre (Acer
saccharum Marsh.) et de cerisier noir (Prunus serotina Ehrh.) de tous les grades NHLA.
Cette base de donnée a été crée et est maintenue par le CRIQ. Un carnet de commande
standardisé a été défini à partir d’usines typiques de producteurs de composants du Québec.
La plupart des simulateurs industriels se basent sur une optimisation « un axe » et produisent
des délignures ou des tronçons qui sont re-traités par une deuxième opération. Contrairement
à ROMI-RIP et à ROMI-CROSS (Thomas 1996a, 1997), l’Optimiseur deux axes (Caron
2001) permet de produire une solution de débitage optimale (pour la période de temps
d’optimisation allouée) en considérant simultanément les deux axes d’optimisation et génère
une solution contenant une série de pièces produites, une série de positions de coupe et des
informations sur les produits en sortie.
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L’Optimiseur deux axes du CRIQ supporte les différents procédés de base de l’industrie :
1) Tronçonnage en tête
2) Délignage en tête
3) Tronçonnage toutes largeurs (panneaux)
4) Délignage toutes longueurs (bois de plancher)
L’Optimiseur deux axes permet de trouver le placement optimal de pièces à l’intérieur de
planches comportant un certain nombre de défauts. La recherche de la solution optimale est
faite en utilisant un parcours de graphe exhaustif pour chaque possibilité de placement pour
un grade donné. Les placements sont faits premièrement selon l’axe primaire de coupe et
deuxièmement en remplissant les espaces libres dans la planche. Une limite de temps
maximale est allouée à la recherche de la solution. Les débits à produire proviennent d’un
carnet de commande de l’usine. L’optimiseur réalise les quantités demandées dans le carnet
de commande et cherche à optimiser le placement dans le but de maximiser l’utilisation du
bois ou de maximiser la valeur monétaire totale produite (Caron 2001). L’optimiseur est
conçu pour être intégré à un équipement spécialisé (mode production) ou pour fonctionner en
mode autonome afin de réaliser des simulations. Dans cette étude, il est important de faire
une distinction entre les différents procédés de débitage permis par l’Optimiseur deux axes. Il
peut générer des solutions de débitage basées sur un procédé conventionnel, respectant un
seul axe de coupe, soit le délignage en tête ou le tronçonnage en tête. Ces procédés sont
appelés: « non-sélectifs ». Il peut aussi, faire une sélection entre deux solutions de débitage
sur les deux axes de coupe, soit la meilleure solution de délignage en tête ou de tronçonnage
en tête. Ces procédés sont appelés: « sélectifs ». De plus, il permet de composer des
procédés de débitage hybrides qui combinent plusieurs types de produits dans la même
solution, en optimisation selon le rendement matière et selon le prix
Le but de cette recherche est de comparer les effets d’une optimisation selon le prix par
rapport à une optimisation selon la surface. Cette comparaison est effectuée en bâtissant une
structure de prix formelle provenant des standards dimensionnels de la norme NHLA et des
prix du marché. L’analyse est faite en considérant des procédés conventionnels de débitage
(délignage en tête, tronçonnage en tête), et une solution de débitage développée par le CRIQ
© Martin Caron
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résultant en un choix entre la meilleure solution du délignage en tête et du tronçonnage en
tête. La difficulté et la nécessité du choix entre le tronçonnage en tête et le délignage en tête
au niveau du rendement matière ont fait l’objet d’études et discussions antérieures (Mullin
1990, Wiedenbeck 2001) mais l’effet que ces procédés ont sur la valeur produite des pièces
est relativement inconnu. Le but de cette étude est de scruter ces différents procédés sous
l’angle de la valeur monétaire produite, en bâtissant une structure de prix permettant de
comparer des optimisations selon la surface avec des optimisations selon le prix. Le
rendement matière, la valeur produite (selon les pièces générées), le retour monétaire obtenu
(valeur des pièces produites moins le coût des matières premières) et la distribution des
produits générés seront utilisés comme éléments de comparaison.
Des études récentes ont permis la validation du logiciel ROMI-RIP dans un contexte de
production (Hamner et al. 2002, Thomas et Buehlman 2002). Ces études utilisent des
facteurs statiques et dynamiques inclus dans ROMI-RIP pour contrôler la priorisation de la
production des pièces du carnet de commande (i.e. : la longueur au carré multipliée par la
largeur des pièces, un coefficient à exposant dynamique). L’utilisation d’un facteur modifiant
la pondération d’une pièce par rapport à une autre biaise inévitablement le rendement matière
obtenu (Thomas 1996b, Mitchell 2002). Utiliser un facteur de pondération afin de favoriser
le placement de pièces longues et/ou larges dans une optimisation revient à accepter une
diminution de rendement. Une étude comparant ROMI-RIP avec l’optimiseur de débitage du
CRIQ (Bouffard et al. 2002) indique qu’en optimisation selon la surface, sans facteurs
statiques ou dynamiques, ROMI-RIP favorise le placement de longues et larges pièces, ce qui
semble indiquer l’existence d’un processus interne d’établissement de priorités sur les pièces
du carnet de commande, et non une pure optimisation en rendement matière.
L’hypothèse de la présente étude est que l’utilisation d’une structure formelle de prix basée
sur des prix réels de composants, permettra de contrôler le placement des pièces dans un
simulateur d’une manière rentable, tout en évitant l’utilisation de facteurs complexes ou
dynamiques. De plus, une optimisation maximisant la valeur devrait permettre de générer une
valeur produite par volume entré plus grande qu’une optimisation maximisant le rendement
matière et ce, peu importe le procédé de débitage utilisé.
© Martin Caron
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2. Comparing a value based and a surface
based part prioritization in the hardwood
rough mill.
2.1. Introduction
Traditionally, profitability in the hardwood industry has been evaluated through yield. Due
to high market pressure, the hardwood industry has put a lot of focus on increasing yield in
their operations (Buehlmann 1998). The increasing costs of labor in North America, the
globalization of markets and the coupling of higher lumber cost with the decrease in lumber
quality forced the industry to find new ways to do business and survive (El Radi et al. 1994,
Hoff et al. 1997). For the hardwood component industry, process optimization and product
development are now vital for survival.
The hardwood component rough mill industry is still far behind the general industrial level in
terms of technological innovation (Buehlmann 1998). The industry must go through phases
of improvement, decrease costs, increase productivity and change the traditional way of
doing things which has been a roadblock to innovation (Hoff et al. 1997). Today’s research
in the rough mill goes through software simulation (Thomas 1996a, 1997), vision systems
(Conners et al. 1997), the improvement of rough mill yield by a selection of boards using
specific criteria or upgrading of boards before the cutting operation (Gatchell 1990, Gatchell
and Thomas 1997, 1998, Wiedenbeck and Scheerer 1996, Wiedenbeck 2001, Steele and
© Martin Caron
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Wiedenbeck 1999). Optimization of the rough mill process using part prices versus yield
has been suggested in prior works (Thomas 1996b, Mitchell 2002). A formal method to
generate a pricing structure and the effects of prioritizing the rough mill operation using such
a structure has not yet been studied thoroughly.
The conventional way of processing hardwood is to cut boards in relation to a first axis in
order to produce strips which maximize yield. (Thomas 1996a). These strips are then
reworked to remove unacceptable defects and respect cutting bill requirements while keeping
an acceptable yield. Traditionally, the component rough mill process has been crosscut-first
(Thomas 1997, Buehlmann 1998). Increasing scarcity of large lumber, increasing lumber
price and higher production needs, slowly pushed wood component producers to rip-first.
(Mullin 1990, Wiedenbeck and Scheerer 1996, Wiedenbeck 2001). These processes
(crosscut-first and rip-first) have their respective advantages. Ripping first can more easily
remove defects along the side of the boards or concentrated along a longitudinal strip and
usually allows for higher yields in the intermediate grades of lumber (No. 1 Common and
No. 2A Common). Crosscutting-first can more easily remove defects concentrated on a
relatively narrow zone by removing the zone and usually provides higher yields on boards
with crook and wane and/or of lower grades (No. 3A Common) (Wiedenbeck 2001). The
processes of “rip-first random lengths“ (flooring, mouldings) and “crosscut-first random
width” (pannelling) are extensions of the rip-first and crosscut-first processes that present
specific advantages due to their free dimensional axis.
© Martin Caron
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Process research on rough mills is pursued using various techniques; one of them is through
computer simulation software. A comparative study between the “Two-Axis Optimizer“
(Caron 2001) and the USDA software ROMI-RIP 2.1 and ROMI-CROSS (Thomas 1996a,
1997) has been recently done (Bouffard et al. 2002). This study is based on the CRIQ
“Two-Axis Optimiser” of the BorealScan system, using a database of lumber graded using
the National Hardwood Lumber Association rules. Two different species were used in the
study : hard maple (Acer saccharum Marsh.) and black cherry (Prunus serotina Ehrh.). The
procedure to digitize the database is described in Bouffard et al. (2002). It used crayon mark
reading, with a color camera, of manually identified defects.
Most industrial rough mill simulators are based on a “one axis” optimization technique
producing maximized strip yield reworked by a secondary processing operation. ROMI-RIP,
ROMI-CROSS, and the CRIQ “Two-Axis Optimiser” consider the whole board before
producing an optimal cutting solution. Cutting positions, yields and grade information is
given as output (Thomas 1996a, 1997, Caron 2001).
The CRIQ “Two-Axis Optimiser” can simulate the following industrial processes :
1) Crosscut-first.
2) Rip-first.
3) Crosscut first, random width (panel).
4) Rip-first, random length (flooring).
© Martin Caron
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This software brings innovative elements such as hybrid solutions containing various types of
products and being able to yield the best solution between two or more rough mill processes.
The are two types of simulation processes used in the simulator, 1) processes based on a
single cutting axes and 2) processes based on a selection between two cutting axes
simultaneously. Processes based on a single cutting axis, namingly rip-first and crosscut-first,
will be called “non-selective” and processes based on a selection between the best of the two
cutting axes will be called “selective”. The software supports different prioritization
strategies such as value, pure yield and yield obtained with static factors applied to parts.
The goal of this study is to compare the effects of an optimization strategy based on the value
of the parts produced versus an optimization based on yield, using a formal pricing structure
based on dimensional standards of the NHLA grading rules and market prices. This analysis
is done considering conventional hardwood component rough mills processes (rip-first,
crosscut-first) and a process selecting the best of rip-first or crosscut-first optimizations
developed by the CRIQ. Yield differences between rip-first and crosscut-first have been the
subject of prior works and discussions (Mullin 1990, Wiedenbeck 2001) but the effects of the
process on the value of the produced parts is relatively unknown. The goal of this study is to
open new research paths using the maximization of value produced instead of the
maximization of yield using different rough mill processes. Results will be compared using
yield, total value produced, return value (value of parts minus the cost of wood) and the
distribution of products generated.
© Martin Caron
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Recent studies validated ROMI-RIP in rough mill operations (Hamner et al. 2002, Thomas
and Buehlman 2002). These studies used static and dynamic prioritization factors included
in ROMI-RIP to control the use of parts in the simulation process (i.e: square length of parts
multiplied by the width, complex dynamic exponent factor applied to parts) because a simple
surface optimization could not compare easily to a real rough mill. Using a factor to modify
the part prioritization in relation to others will inevitably change the final yield obtained
(Thomas 1996b, Mitchell 2002). Applying a surface modification factor to long and/or wide
parts is accepting a reduction of yield according to the specifications of the factor used, this
strategy is also called “force cutting”.
A study comparing ROMI-RIP and the CRIQ “Two-Axis Optimiser” (Bouffard et al. 2002)
has shown that in pure yield optimization, without any prioritization factors, ROMI-RIP was
still prioritizing long and wide parts over smaller ones. This result was counter-intuitive and
points to the existence within ROMI-RIP of an internal prioritization mechanism. This
mechanism appears to apply even when the pure area prioritization strategy is chosen.
It is our belief that the use of an explicit pricing structure will allow a better solution to the
prioritization of parts while avoiding the use of prioritization factors that are not necessarily
based on sound theoretical ground and may yield unexpected results. Furthermore, it is
believed that the use of a part prioritization strategy based on value of parts should provide
results with a higher return value than a pure area prioritization strategy, and that whatever
the NHLA lumber grade used.
© Martin Caron
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2.2. Materials and methods
2.2.1. Definitions
Volume of input
The volume of input is the volume of lumber, accounted in board feet, entered in the
simulation process (void defects are not included).
Volume of output
The volume of output is the volume of parts, accounted in board feet, produced in the
simulation process.
Yield
Yield in this study accounts to the “ratio of the surface area of output to the surface area of
input of common thickness expressed as a percent” (Buehlmann 1998).
Value produced
Value produced in this study accounts to the total value of part produced expressed in US
dollars.
© Martin Caron
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Value produced per board feet entered
Value produced per board feet (VPBF), accounts to the total value of parts produced divided
by the input volume entered to produce those parts. By dividing the return by the B.F entered
we can get a VPBF value independent of simulation B.F variations.
Return
The return is a monetary value that accounts to the cumulated value of parts produced in a
simulation minus the cost of the wood entered to produce those parts expressed in US dollars.
Return per board feet entered
Return produced per board feet (RPBF) for the total value of parts produced in the simulation
minus the cost of the wood used to produce those parts divided by the input volume. By
dividing the return by the B.F entered we get a RPBF value independent of simulation B.F
variations. This is an integrated value combining the value of parts produced, the number of
parts produced, the volume of wood inputed and finally the cost of the wood used to produce
those parts. This performance indicator is believed to be the most meaningful for comparing
different rough mill processes with a similar pricing structure for parts in our study.
2.2.2. Board database description
The board database used was built using the image acquisition system BorealScan built by
the Centre de Recherche Industrielle du Québec. All boards are contained within three
NHLA grades (No. 1 Common, No. 2A Common and No. 3A Common) and two species
(hard maple and black cherry) representative of the supply of a specific component
© Martin Caron
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manufacturer from Quebec. Boards were graded by NHLA certified graders within the
standard tolerances of the industry.
All boards were dried rough wood. The boards were not ripped prior to scanning nor planed.
Various color crayons were used to mark defects on both faces of the boards. Hand-marks
placed by the operators were then scanned in the BorealScan system. As such, the precision
of defect detection was limited to the precision of the hand-markers and the size of the
crayon used (3mm wide).
Three defect types were originally marked on the boards : 1) unacceptable defects (wane,
large splits, large holes, unsound knots), 2) colorations defects (heartwood), and 3) sound
defects, that are acceptable on the back face of certain products (sound knots, burl, etc...).
Scanning of boards was done with two high speed, high resolution color cameras providing
images with a resolution of 0.2 mm along the width of boards and 1 mm along the length.
A digitized file representing the type and position of the defects of each board was obtained
in output. The lower left corner was used as a reference point for all boards. The
BorealScan image acquisition system is shown in figure 1.
All marked defects were thoroughly re-worked using an image processing software. Images
were manually modified to change the type of certain defects according to the specifications
of Quebec component stock factories. Thus, coloration (heartwood) was split into two
different types of defects 1) light coloration (heartwood where contrast with sapwood is
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minor), 2) dark coloration (heartwood where contrast with sapwood is major). Knots of less
than 3mm in diameter were made a separate class as “pin knots”. The same images and
database were used in a study comparing the CRIQ optimiser, ROMI-RIP and ROMI-CROSS
(Bouffard et al. 2002).
Information on the boards contained in the database is detailed in Table 1. From the
observation of the database, the following comments can be made:
•
No 1 Common black cherry is of lower average length than the No 1 Common hard
maple.
•
No 3 Common hard maple is of lower average length than the No 3 Common black
cherry.
•
Standard deviation on length and width is higher for hard maple than for black
cherry, especially for lower grades.
Black cherry boards were precision trimmed and sorted by length. The black cherry database
contained fixed lengths of four, five and six feet for No. 1 Common and lengths of seven and
eight feet for No. 2A and No. 3A Common. Hard maple boards were also trimmed and sorted
by length. Boards of six, seven and eight feet were used in No. 1 common, boards of four,
five, seven and eight feet were used in No. 2A Common and boards of four, five, six, seven
and eight feet were used in No. 3A Common. The average length of the No. 1 Common
black cherry is sensibly shorter than No. 2A Common and No. 3A Common. There is more
variability on lengths in the hard maple database because there are more lengths included.
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It is important to note that there is no information in the database concerning the crook of
boards. All board dimensions were determined by the smallest rectangle encompassing the
wooden section of the board. Void defects were included to represent the sections in the
rectangle where no wood was scanned. Void sections were not considered in the input
surface used for yield calculations in the simulator.
2.2.3. Part grade definition
The part grades used in the simulator can be found in Table 2. They are sorted from the
highest priority to the lowest. There are 7 different grades based on 3 general product types.
The product specifications were based on actual products from a typical Quebec component
factory. The three types of products used were fixed dimensions components, random width
panels and random length flooring strips.
2.2.4. Defects definition
There were three defect types determined and based on the actual rough mill process of a
Quebec component factory (coloration, sound defects and unacceptable defects). Two defects
were manually added with the image processing software. Coloration was split into “light
heartwood” and “dark heartwood”. “Pin knots” were extracted from the sound defects class.
A sixth defect type (void) was added by the image processing software. Table 3 describes the
defect types used in the rough mill simulator.
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2.2.5. Part grades in relation with defects
Table 4 describes the relationships between part grades and defects used in the rough mill
simulator. The “―“ symbol means that this type of defect cannot be accepted on a part of
this grade. The “1F” symbol means that this defect can be accepted on the back face of parts
of this grade. Finally, the “2F” symbol means that this defect can be accepted on both faces
of a part of this grade. These relationships were built according to the rough mill
specifications of a Quebec component factory.
2.2.6. Cutting bill
Various cutting bills were obtained from the same rough mill in Quebec. Based on these
existing cutting bills, a standardised cutting bill was built for this study and it is an extension
of the cutting bill used by Bouffard et al. (2002). Standardization was performed to reduce
the number of actual lengths and widths into a limited number of dimensions that were easier
to manage by the simulator but were still covering the actual range of dimension values. The
cutting bill requests for three different types of products in infinite quantities. The cutting bill
contains 8 different widths (from 45mm to 125mm) and 15 different lengths (from 290mm to
1800mm). Panel parts width were variable and ranged from 38mm to 76mm. Flooring parts
length were variable and ranged from 350mm to 1850mm. Two grades are used for fixed
dimension components (1F and 2F), two grades are used for panel parts (Pan 1F and Pan 2F)
and three grades are used to define parts of flooring (Sup, Med, Inf). The complete cutting
bill can be seen in Table 5.
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2.2.7. The pricing structure
The use of a pricing structure is to provide a solid ground for the prioritization of parts while
using a value based prioritization mechanism. With a pricing structure and a value
prioritization mechanism, the usage of higher value parts is favored. Previous studies
mentioned that a pricing structure applied to a cutting bill could emulate a dynamic part
prioritization using dynamic weight factors (Thomas 1996b, Mitchel 2002). In order to build
the pricing structure it was necessary to determine what attributes make a part more or less
wanted. Three attributes were determined as being responsible for the value of parts : 1) the
length, 2) the width, and 3) the grade. Rules defining the pricing structure in the present
study were based on the establishment of relationships between parts attributes, NHLA
hardwood grading rules and NHLA lumber value.
2.2.8. NHLA dimension definitions
The pricing structure aims at establishing a formal base for defining a way to generate prices
for every part in a cutting bill. Hence the price structure was not based on single part
novelties or producer niche or market specialization. The pricing structure was rather based
on dimensions from cuttings used in NHLA hardwood grading which is the core of the
current hardwood trade. In NHLA grading, length, width and positioning of defects are used
to determine the grade of each board. Three different length groups and three different width
groups were built based on the NHLA hardwood grading cutting dimensions per grade and
the standardized cutting bill. NHLA standard cutting dimensions are summarized in Table 6.
Table 7 describes the dimension limits used to qualify pieces of the standardized cutting bill
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parts in the pricing structure established, based on similar relationships as seen in the NHLA
grading system.
2.2.9. Value in the pricing structure depend on parts dimensions
NHLA grading is the trade standard for the hardwood industry, thus every NHLA grade has
an average price value attached to it in the market. Pricing differences between NHLA
lumber grades are used in the pricing structure to create factor steps for the dimension
groups. The pricing structure is based on formal steps aiming at being as close as possible to
reality. The pricing structure is not a universal and an exact representation of what is
happening in the market, but these factors could be modified in the simulation of a specific
mill to map exactly a specific pricing structure.
Table 8 shows the prices of green, unselected, hard maple. A dimension group will be linked
to a specific NHLA grade to determine a basic level of comparison. Every other dimension
group and grade will be compared to this basic level to determine its pricing factor. Thus,
every part having dimensions or grade lower than this basic level will receive a penalty factor
and every part having dimensions or grades higher than this basic level will receive a bonus
factor. There is an important difference between lengths in the NHLA cuttings and a much
smaller difference between widths. To determine pricing factors, “Short” will be linked to
No. 2A Common because the “Short” group lengths are similar to the cutting lengths of No.
2A common. In the same way, “Medium” will be linked to No. 1 Common and “Long” will
be linked to FAS / Select. No. 2A Common width dimensions are narrower compared to the
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other NHLA cutting width dimensions so 2A Common will be linked to “Narrow” in the
width factors, No.1 Common will be linked to “Medium” and FAS / Select to “Wide”.
A pricing factor needs to be given to grades of parts. Aside from a few special rules for
positioning of splits and wane (NHLA 1998), all NHLA grades have a similar acceptation of
defects. All NHLA cuttings are considered to have one clear face and one sound face. Those
characteristics compare generally to the basic grade 1F used in the simulator. To determine
the pricing factor scale between all seven grades, a survey was made from five Quebec rough
mills to acquire average panel and fixed dimension components prices. A trade journal
(Hardwood review weekly 2002) was used to determine prices for random length strip
flooring. The Quebec Wood Export Bureau was consulted to examine and validate the
pricing structure obtained (Labbé 2002). Pricing factors obtained for each grade are
presented in Table 9.
Product prices were obtained from standard furniture component and cabinet factories in
Quebec, these produced part prices did not contain specifications for black cherry which is
more of a marginal product. Thus prices for hard maple products were used in all grades
specifications, including those for black cherry. Lumber prices were hence those of
unselected hard maple based on a northern pricing and this for both species.
The final price of a part is computed as the unit surface price multiplied by the part surface
and by the total computed part factor (the length factor multiplied by the width factor
multiplied by the grade factor). The base unit price (1.0) was priced at US$ 1700 per 1000
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B.F of hard maple 1F components. The price of each part can be computed using this
information.
Using these guidelines and the pricing scale we can allocate a dollar value to all the parts in
the standardised cutting bill. For example, a piece of 290 mm long and 65 mm wide of grade
2F would have a price of 0.548$. A 1600mm long piece by 65 mm wide of 2F grade would
be allocated a price of 7.988$.
2.2.10. Simulation process
Fifteen randomly generated board files each containing 65 boards (which amounts to
approximately 200 B.F per file) were built for each wood species and NHLA grade.
Optimization data was gathered in two prioritization modes (yield, and value) and three
optimization processes (rip first, crosscut first, best of rip first / crosscut first).
General optimization parameters are summarized in Table 10. All simulations were allowed
60 seconds per board to execute, after which time the simulator would return the best cutting
solution found. Simulations using the process of “best of rip-first / crosscut-first” were
allowed 120 seconds to keep the time per process constant.
2.2.11. Statistical analysis
Simulation results were analysed using a two-way factorial ANOVA design. The
significance of the prioritization strategy, the process and their interactions were examined in
the model. Two different wood species and three NHLA grades were used which accounted
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to 6 ANOVA analyses. Fifteen observations were made per test (n=15). The statistical
threshold used of significance was fixed at 99 %.
All ANOVA tests presented here are based on the null hypothesis that “ the effects of the
prioritization strategy and the rough mill process are equal for a given NHLA grade and a
species of wood and have no effects on the simulation factor analysed (yield, value produced,
return…)”.
The factorial ANOVA model used is the following :
y ijk = µ + α . j + β i. + γ ij + ε ijk ,
Where:
1.
yijk is the matrix of observations.
2. µ is a matrix containing the averages of the observations (over-all means).
3. α . j is the prioritization effect.
4. β i. is the process effect.
5. γ ij is the interaction term.
6. ε ijk is the error term.
Since our ANOVA analyses were comparing three processes with different values, a
significant process difference noted in the ANOVA results did not necessarily mean that two
optimization results from two different processes were significantly different when looked
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together. So paired t-tests (α<0,01) were done to compare results between each process
values in our ANOVA table.
The two way classification analysis is based on the Student t test. Thus, for this test to be valid,
variances associated with different observations must be homogeneous. Cochran’s test for the
homogeneity of variances will be used to determine if variances are equal. The computation with
the variances S is the following (Bowker and Lieberman 1972) :
G=
highest S 2
,
S12 + S22 +L+ Sk2
where k is the total number of observed variances. Homogeneity of variances is accepted if
G ≤ gα (n) , where g α is given in a Cochran’s chart (Bowker and Lieberman 1972) and n is
the number of repetitions (n=15). Cochran’s test proved the homogeneity of variances, hence
the ANOVA could be performed validly.
2.3. Results and discussion
The statistical analysis has shown that the prioritization strategy and the rough mill process
both have significant effects on the output values from the simulator. The effect of the
prioritization strategy on output values was the highest, followed by the optimization process.
Moreover, it was seen that there is always an interaction between the prioritization and the
rough mill process used, at a significance level of α ≤ 0.01. The interaction means that there
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is a direct relationship between the prioritization mechanism and the criterion evaluated, but
this effect is different across processes.
Three performance indicators have been used to compare results between the different
simulations : 1) yield, 2) VPBF and 3) RPBF. Yield and RPBF will be discussed and
examined in this section. VPBF is necessary as an intermediate result providing the total
value of parts produced in a simulation for the calculation of RPBF. The optimization results
are shown on Table 11 for hard maple and Table 12 for black cherry in all three NHLA
grades (No.1 Common, No.2 Common and No.3 Common). The intuitive reasoning behind
value prioritization suggested by Mitchell (2002) has been proven by the results. Simulations
using value prioritization produced significantly less yield than simulations using surface
prioritization, but produced significantly more VPBF, thus resulting in a much higher overall
RPBF.
General product distribution follows the products priority requirements. The most valuable
product required in the product mix and the one that was most produced was dimension stock
(Figures 2 and 3). Panels and flooring were used to fill gaps and improve overall value.
Dimension stock production decreased while flooring production increased as lower NHLA
grade lumber was processed in the simulations. Panel production also increased with a lower
NHLA grade but to a lesser extent than flooring.
The black cherry database produced a lower RPBF value in the intermediate and lower
grades (No.2 and No.3 Common) than the hard maple which is confirmed by the product
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distribution. There appears to be a difference between black cherry and hard maple
considering the proportions of dimension stocks and flooring components produced. Black
cherry produced less dimension stock and more flooring in all NHLA grades than hard
maple. These differences are explained by the higher proportion of dark heartwood and
sound defects (sap stain mostly) in the black cherry lumber.
2.3.1. Effect of the processes on yield
Using a surface prioritization mechanism for hard maple, the highest yield has been obtained
from the selective process in all lumber grades. Between the two “non-selective” processes,
rip-first has provided the best yield in No.1 Common. Yields were similar for both “nonselective” processes in No.2 Common. Crosscut-first provided the best yield for No.3
Common.
Using a surface prioritization mechanism for black cherry, the highest yield has been
provided by the selective process in all lumber grades. Between the two “non-selective”
processes, rip-first has provided the best yield in No.1 and No.2 Common lumber. Crosscutfirst provided the best yield for No.3 Common lumber.
Yield results using a surface prioritization mechanism are higher in both species using a ripfirst process in the higher grade of lumber used (No.1 Common). The difference between
yields of the two "non-selective" processes diminished for No.2 Common and even
disappeared for hard maple. For No.3 Common, crosscut-first provided higher yields for
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both species. These results confirm the relationships suggested by Wiedenbeck (2001) in her
crosscut-first and rip-first comparison. The results also show the high yield conservation
potential provided by a "selective" process. The yield is kept higher by the "selective"
process which adapts more easily to board dimensional and defects characteristics. For
example, using the rip-first process, yields varied from 73.4% to 61.7% between No.1
Common and No.3 Common. With the crosscut-first process, yields varied from 69.2% to
64.4%. By using the selective process, yields varied from 75.2% to 67.5%. The results tend
to show that a selective process based solely on yield, used in the rough mill would provide a
systematic higher yield and also give results that are more robust to lumber mix variations.
Using a value prioritization mechanism for hard maple, the best yield giving process for No.1
Common lumber was the selective process, followed by the rip-first process, and finally the
crosscut-first process. For No.2 and No.3 Common, the best yield giving process was
crosscut-first, followed by the selective process and then finally the rip-first process.
Using a value prioritization mechanism for black cherry, the best yield giving process for
No.1 Common was the selective process, followed by the rip-first and crosscut-first
processes which provided similar results. For No.2 Common, the crosscut-first process and
the selective process were providing similar yields, followed by the rip-first process. For
No.3 Common lumber, the best yield was provided by the crosscut-first process, followed by
the selective process and finally the rip-first process.
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In the value prioritization mechanism, yield was not the criterion being maximized, thus
yield results vary depending on the “value adding” potential brought by an addition of yield
for each process. The "selective" process did not provide the best yields in all lumber grades,
because the selection between rip-first solutions and crosscut-first solutions were done on the
basis of value, not yield. Despite the fact that prioritization was not done on yield but value,
yield results between the two "non-selective" processes showed a similar trend to the one
observed when using a prioritization mechanism based on the maximization of surface. The
rip-first process provided higher yield in the higher NHLA grade used, the gap between the
two "non-selective" processes diminished in the intermediate grade (No.2 Common) while
slightly being in favor of the crosscut-first process, and finally, the crosscut-first process
provided a higher yield in the lower NHLA grade (No.3 Common). For example, yields
obtained using No.1 Common hard maple were 65.6% in rip-first and 61.8% in crosscut-first.
For No.2 Common, a yield of 64.7% was obtained in crosscut-first and 62.3% in rip-first.
For No.3 Common, a yield of 59.5% was obtained in crosscut-first and 56.6% in rip-first.
The best overall yield for both species and for both prioritization mechanisms were obtained
using No.1 Common with a selective process.
2.3.2. Effect of the process on RPBF
Using a surface prioritization mechanism for hard maple with No.1 Common lumber, the
highest RPBF was provided by the selective process, followed by the rip-first process and
finally the crosscut-first process. For No.2 Common, the highest RPBF were provided by the
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rip-first and selective processes whose values were similar, followed by the crosscut-first
process. For No.3 Common, the highest RPBF was provided by the selective process,
followed by the rip-first and crosscut-first processes which provided similar values.
Using a surface prioritization mechanism for black cherry with No.1 Common lumber, the
highest RPBF was provided by the rip-first process, followed by the best of rip-first and
crosscut-first process and finally crosscut-first. For No.2 Common, RPBF obtained from the
three different processes were not significantly different. For No.3 Common, highest RPBF
were provided by the best of rip-first and crosscut-first and crosscut-first processes which
provided similar values, followed by the rip-first process.
Using a value prioritization mechanism for both species with No.1 Common and No.2
Common lumber the highest RPBF were obtained from the best of rip-first and crosscut-first
and rip-first processes which provided similar values, followed by the crosscut-first process.
For No.3 Common, the best RPBF giving process was the best of rip-first and crosscut-first
process, followed by the rip-first process and finally the crosscut-first process.
Value, and thus RPBF are not maximized using a surface prioritization mechanism. Results
show only a small variation between the highest RPBF and the lowest RPBF obtained. This
little variation is directly caused by the fact that value was not the criterion being maximized,
thus parts produced have no prioritization on length and grade. Thus smaller pieces and
lower grades are used to fill boards and obtain the highest possible yield. Comparing the two
"non-selective" processes, rip first provided the best RPBF in the higher grades of lumber
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used (No.1 Common). For No.2 Common, the gap between the RPBF from the rip-first and
crosscut-first processes diminished. For No.3 Common, crosscut-first provided the best
RPBF. Since yield is being maximized these results follow the same trend and point directly
to the results obtained from yield in a surface prioritization. For example, using No.1
Common hard maple, RPBF obtained in rip-first was 1.05 and in crosscut-first 0.95. For
No.2 Common, a RPBF of 1.34 was obtained in rip-first and 1.29 in crosscut-first. For No.3
Common, a RPBF of 1.24 was obtained in rip-first and 1.23 in crosscut-first.
RPBF results obtained using a value prioritization mechanism clearly show the "value
adding" potential of the rip-first process in both species of lumber used. The variation of
RPBF obtained between the two "non-selective" processes is very important and always in
favor of the rip-first process except for No.3 Common black cherry. For No.1 Common and
No.2 Common, rip-first provides a similar RPBF value to the selective process. For No.3
Common, the selective process provided a higher RPBF. than the rip-first process for both
species. These results clearly point to a higher RPBF produced in a rip-first process for the
higher and intermediate grades of lumber (No.1 and No.2 Common) . There are two possible
sources for this significant increase of produced value :1) a longer average length of parts
produced, 2) a higher average grade of parts produced. Results observed gave no insights or
information pointing to a higher grade of parts being produced in rip-first versus crosscut
first. This higher value is obtained from longer parts being produced from boards which are
cut along their length axis in a rip-first process coupled with a value based part prioritization.
For example, using No.1 Common hard maple, RPBF obtained in rip-first was 2.18 and in
crosscut-first 1.60. For No.2 Common, a RPBF of 2.24 was obtained in rip-first and 1.79 in
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crosscut-first. For No.3 Common, a RPBF of 1.73 was obtained in rip-first and 1.55 in
crosscut-first.
The best overall RPBF for both species was obtained with the value prioritization mechanism
with a rip-first or selective process. For hard maple, the best RPBF value was obtained for
No.2 Common and for black cherry, the best RPBF value was obtained for No.1 Common.
2.3.3. Effect of the prioritization mechanism on yield
Switching from a surface prioritization mechanism to a value prioritization mechanism for
No.1 Common hard maple caused a significant yield diminution of 10.6% in rip-first, 10.7%
in crosscut-first and 12.0% in the selective process. For No.2 Common hard maple switching
prioritization mechanisms accounted for a significant yield diminution of 11.9% in rip-first,
8.4% in crosscut-first and 13.8% in the selective process. For No.3 Common hard maple the
change of prioritization mechanism accounted for a significant yield diminution of 9.9% in
rip-first, 7.6% in crosscut-first and 13.2% in the selective process.
The change of prioritization mechanism from surface to value for No.1 Common black
cherry accounted for a significant yield diminution of 14.8% in rip-first, 13.9% in crosscutfirst and 16.8% in the selective process. For No.2 Common black cherry, switching
prioritization mechanism from surface to value produced accounted for a significant yield
diminution of 13.7% in rip-first, 9.2% in crosscut-first and 15.9% in the selective process.
For No.3 Common black cherry the change of prioritization mechanism accounted for a
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significant yield diminution of 13.2% in rip-first, 9.0% in crosscut-first and 16.9% in the
selective process.
For both species, the "selective" process suffered from the highest yield diminution by
changing the prioritization mechanism from the maximization of surface to the maximization
of value. The results have also shown that between the "non-selective" processes, rip-first
suffers from a higher yield diminution than crosscut-first with a change of prioritization
mechanism.
These results support the basic hypothesis of this study and are in agreement with the
comments by Mitchell (2002). Switching from a surface based part prioritization mechanism
to a value based part prioritization mechanism has the overall effect of decreasing obtained
yield. The results also proved that a value based part prioritization has an important effect on
the selection of parts produced because of the important yield variations observed.
2.3.4. Effect of the prioritization mechanism on RPBF
Switching from a surface prioritization mechanism to a value prioritization mechanism for
No.1 Common hard maple caused a RPBF increase of 107.6% in rip-first, 68.4% in
crosscut-first and 101.8% in the best of rip-first and crosscut-first. For No.2 Common hard
maple, the change of prioritization strategy accounted for a RPBF increase of 67.2% in ripfirst, 38.8% in crosscut-first and 68.6% in the best of rip-first and crosscut-first. For No.3
Common hard maple, the change of prioritization strategy accounted for a RPBF increase of
39.5% in rip-first, 26.0% in crosscut-first and 43.8% in the best of rip-first and crosscut-first.
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The change of prioritization strategy from yield to value for No.1 Common black cherry
accounted for a yield diminution of 115.2% in rip-first, 101.5% in crosscut-first and 133.7%
in the best of rip-first and crosscut-first. For No.2 Common black cherry the change of
prioritization strategy accounted for a RPBF increase of 65.6% in rip-first, 38.9% in crosscutfirst and 66.7% in the best of rip-first and crosscut-first. For No.3 Common black cherry, the
change of prioritization strategy accounted for a yield diminution of 41.9% in rip-first, 29.0%
in crosscut-first and 43.6% in the best of rip-first and crosscut-first.
These results proved the second part of the study hypothesis. RPBF, is considerably higher,
when prioritizing parts with a value prioritization mechanism rather than a surface
prioritization mechanism. The results also confirmed that a value based part prioritization has
an important effect on the selection of parts produced. Obtaining a much higher produced
RPBF while observing a yield diminution proves that the selection of parts is based on a
sound basis which is the pricing structure used for parts. For both species, the results point to
higher benefits from switching from a surface to a value prioritization mechanism for the
higher NHLA grades, the obtained benefit tends to decrease with grade.
Results have shown for both species that surface prioritization gave the smallest RPBF from
the higher grades of lumber. This means that while yield obtained was high, products issued
from a surface prioritization mechanism were clearly composed of parts of low value (shorter
and lower grade parts). This result is important because it proves that an increase of yield
obtained from a complex cutting bill does not necessarily provide an increase of RPBF. The
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maximization of surface does not provide the best value obtained from the board in a rough
mill, because hardwood pricing is based on length, width, and grade. As shown by this
results on RPBF both concerning the effects of the process and the effects of prioritization,
the maximization of surface tends to produce shorter or lower grade parts.
Results also confirmed the strength of RPBF in comparing different rough mill processes,
producing different product mixes and using different grades of lumber as input. They have
shown that yield is a complex and potentially incomplete value when used as a performance
index between different rough mills. Yield by definition does not contain information on the
produced parts, the volume of wood required for production nor the cost of wood used for the
production.
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2.4. Conclusions
This study was made to compare the effects for hard maple and black cherry of prioritizing
rough mill operations with the maximization of value using a part pricing structure inspired
by the NHLA hardwood grading rules, versus the prioritization of rough mill operations
based on pure yield.
The comparison required the examination of the direct effects and interactions of the three
rough mill processes (rip-first, crosscut-first and best of rip-first and crosscut-first) and the
two prioritization strategies (maximization of the yield, maximization of the value) used in
the CRIQ “Two Axis Optimiser”.
Overall results confirmed the hypothesis in favor of a valued based part prioritization
strategy. Simulations using the value prioritization mechanism produced less yield than
simulations using the surface prioritization mechanism, but always produced significantly
better RPBF. This result is important because it emphasizes an aspect that has not been
thoroughly studied in past work. Higher yield is not directly related to a higher monetary
return value. The current standard for rough mill performance (i.e. yield) is based on a result
that is not always producing the optimum value of parts, thus not giving the component
rough mill plant the best possible value for the wood it is using.
The yield results obtained from a surface prioritization mechanism have been higher in both
species using rip-first in the higher grade of lumber used (No.1 Common). The difference
between yields of the two "non-selective" processes was smaller for No.2 Common and even
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non-significant for hard maple. Crosscut-first provided higher yields for both species in the
lower grade of NHLA lumber used (No.3 Common).
The results also show the high yield conservation potential provided by a "selective" process
like the best of rip-first and crosscut-first process with a surface maximization mechanism.
Even as lower NHLA grade lumber is processed, the yield is kept higher by the "selective"
process which adapts more easily to board characteristics and dimensions. This tends to show
that a selective process used in the rough mill would provide a constant higher yield and also
give yield results more robust to lumber mix variations, when yield is used as a performance
indicator.
These results clearly point to a higher RPBF produced in a rip-first process for the higher and
intermediate grades of lumber (No.1 and No.2 Common) . There are two possible sources
for this significant increase of produced value :1) a longer average length of parts produced,
2) a higher average grade of parts produced. Results observed gave no insights or
information regarding the production of parts of a higher grade in rip-first versus crosscut
first. This higher value is obtained from longer parts being produced from boards which are
cut along their length axes in a rip-first process coupled with a value based part prioritization.
This study has confirmed the strength of RPBF in comparing different rough mill processes,
producing different product mixes and using different grades of lumber as input. They have
shown that yield is a complex and potentially incomplete index to evaluate performance
between different rough mills. Yield by definition does not contain information on the
© Martin Caron
34
produced parts, the volume of wood required for the production nor the cost of wood used for
the production.
It is important to note that this study was done requiring infinite part quantities because of the
component producer context it was linked to. The results obtained with infinite quantities
point to the use of a pricing structure for parts in a simulator with value-based part
prioritization as a means to provide better results than a “pure yield” strategy.
Future work could be done comparing the effects of part prioritization between value and
surface using finite cutting bills to determine the potential industrial use of a pricing structure
in other production contexts such as the furniture industry.
© Martin Caron
35
2.5. Litterature cited
Bouffard J.-F., Beauregard R. et T. Lihra. 2002. Hardwood rough mill optimization :
comparison of various approaches and simulation software. Minutes du 4ième Groupe de
travail de l’IUFRO WP S5-01-04 « Connection between Forest Ressources and Wood
Quality : Modelling Approaches and Simulation Software », Harrison (Vancouver) BC, 9-13
septembre 2002.
Bowker, A. H. et G.J. Lieberman.1972. Engineering statistics 2nd Edition. Prentice-Hall
press. 1972. p264.
Buehlmann, U. P. 1998. Understanding the relationship of lumber yield and cutting bill
requirements : a statistical approach. Doctoral dissertation, unpublished, Virginia Polytechnic
Institude and State University, Blacksburg, VO, 209p.
Conners, R. W., D. E. Kline, P. A. Araman, an T. H. Drayer 1997. Machine vision
technology for the forest product industry. IEEE computer innovative technology for
computer professionals 30(7):43-48.
Caron, M. 2001. Optimiseur 2 axes : Manuel de l’utilisateur [CRIQ rough mill simulator
user's guide in french]. Centre de Recherche Industrielle du Québec. Sainte-Foy, Quebec.
10-12-2001. 55p.
© Martin Caron
36
El-Radi, T. E., S. H. Bullard and P. H. Steele. 1994. A performance evaluation of edging and
trimming operations in U.S. hardwood sawmills. Canadian Journal of Forestry Research
23:1450-1456.
Gatchell, C. J. 1990. The effect of crook on yields when processing narrow lumber with a
fixed arbor gang ripsaw. Forest Products Journal 40(5):9-17.
Gatchell, C. J. and R. E. Thomas. 1997. Within-grade quality differences for 1 and 2A
Common lumber affect processing and yields when gang-ripping red oak lumber. Forest
Products Journal 47(10):85-90.
Gatchell, C.J and R. E. Thomas. 1998. Data bank for kiln-dried red oak lumber. Research
paper NE-245 USDA Forest service, Northeastern Forest Experiment Station, Radnor, PA.
60p.
Hamner, P. C., Bond, B. Wiedenbeck, J. 2002. The effect of lumber length on part yields
in gang-rip-first rough mills. Forest Products Journal 52(5):71-76.
Hardwood review weekly. 2002. Hardwood pricing, hard maple green unselected, northern
pricing. March 2002. Vol. 18. Issue 28.
© Martin Caron
37
Hoff, K., N. Fisher, S. Miller, and A. Webb. 1997. Sources of competitiveness for secondary
wood products firms : A review of literature and research issues. Forest Products Journal
47(2):31-37
Labbé S. 2002. CEO of the Québec Wood Export Bureau. Personnal communication.
Mitchell, P. 2002. Setting priorities on rough mill cutoff saws. Wood Digest May 2002 :
41-45.
Mullin, S. 1990. Why switch to rip-first ? Furniture design & manufacturing, 62(9):36-42.
National Hardwood Lumber Association. 1998. NHLA 1998 Hardwood Grading rules and
inspection handbook. Memphis, TN, USA. 90p.
Steele, H. P. And Wiedenbeck, J. K. 1999. The influence of lumber grade on machine
productivity in the rough mill. Forest products Journal 49(9) : 48-54.
Thomas, R. E. 1996a. ROMI-RIP : An analysis tool for rip-first rough mill operations.
Forest Products Journal 46(2):57-60.
Thomas, R. E. 1996b. Prioritizing parts from cutting bills when gang ripping first. Forest
© Martin Caron
38
Thomas, R. E. 1997. ROMI-CROSS : An analysis tool for crosscut-first rough mill
operations. Forest Products Journal 48(2):68-72.
Thomas, R. E. And Buehlmann. U. 2002.Validation of the ROMI-RIP rough mill simulator.
Wiedenbeck, J. K. 2001. Deciding between crosscut and Rip-first processing. Wood and
Wood Products August 2001. 5p.
Wiedenbeck, J. K. And C. Scheerer. 1996. A report on rough mill yield practices and
performances – how well are we doing ? Twenty-fourth annual Hardwood Symposium of the
Hardwood Research Council, 121-128.
© Martin Caron
39
Table 1. — Description of the hard maple and black cherry board database
Average
length
Name
(mm)
Std.
Average
Std.
Average Total
deviation width
deviation lumber lumber
length
width volume volume
(mm)
(mm)
(mm)
(B.F)
(B.F)
Hard maple – 1 Common
2268.56
277.54
159.69
47.38
3.67
503.12
2095.05
449.08
136.63
37.34
2.89
512.19
1734.95
500.68
136.76
35.14
2.37
320.54
Black cherry – 1 Common
1746.43
197.68
157.35
41.00
2.77
529.62
2399.56
128.68
138.47
33.27
3.31
580.01
2412.43
118.90
118.32
24.27
2.78
387.02
Table 2. — Part grades description
Grade name
Product
Example
Pan 2F
Clear panel 2 faces
Kitchen cabinet doors
2F
2 clear face dimension
part
Bed rim part
Pan 1F
Clear panel 1 face
Table tops
1F
1 clear face dimension
part
Front drawer part
Sup
Superior grade flooring
component
« Select & better » flooring part
Med
Medium grade flooring
component
« Traditionnal » flooring part
Inf
Inferior grade flooring
component
« Western » flooring part
© Martin Caron
40
Table 3. — Descriptions of defects in the board database
Defect
Description
Void
Sections around the board that are included in
the rectangular section but do not contain wood
(crook, taper ).
Unacceptable
Defects that were considered high removal
priority by the markers : deep wane, rot, large
splits, holes, shake.
Sound defect
Defects that are keeping the structural integrity
of the board (sound knots, sap stain, mineral
stain...).
Pin knots
Knots that are smaller than 3mm in diameter.
Light
heartwood
Heartwood where colour contrast with
sapwood is light.
Dark
heartwood
Heartwood where colour contrast with
sapwood is heavy.
Table 4 . — Definition of part grades in relation with defects
Grade name
Pan 2F
Pan 1F
2F
1F
Sup
Med
Inf
© Martin Caron
Void
Unacceptable
Sound
defects
Pin knots
Light
heartwood
Dark
heartwood
―
―
―
―
―
―
―
―
―
―
―
―
―
―
―
―
―
―
1F
1F
2F
―
1F
2F
2F
2F
2F
2F
―
1F
―
1F
1F
2F
2F
―
1F
―
1F
1F
2F
2F
41
Table 5. —Cutting bill used in the simulations
Length
(mm)
Width
(mm)
Grade
290
290
290
350
350
350
400
450
450
450
450
450
500
500
500
500
550
600
600
650
650
650
650
750
750
750
850
1050
1050
1050
1050
1250
1250
1500
1500
1500
1600
1600
1600
1600
1800
1800
1800
1800
350
400
450
500
550
650
850
1050
1250
1500
1800
random
random
random
65
76
85
65
70
76
65
45
70
76
85
125
65
70
76
85
105
65
85
55
65
85
105
65
85
125
65
45
65
85
105
65
85
65
75
85
65
75
85
105
55
65
75
85
random
random
random
random
random
random
random
random
random
random
random
65
90
115
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
2F, 1F
Pan2F, Pan1F
Pan2F, Pan1F
Pan2F, Pan1F
Pan2F, Pan1F
Pan2F, Pan1F
Pan2F, Pan1F
Pan2F, Pan1F
Pan2F, Pan1F
Pan2F, Pan1F
Pan2F, Pan1F
Pan2F, Pan1F
Sup, Med, Inf
Sup, Med, Inf
Sup, Med, Inf
© Martin Caron
42
Table 6. — Cutting dimensions in the NHLA grading system
Length
(mm)
Width
(mm)
2 Common
610
51
1 Common
915 / 610
75 / 102
2133 / 1524
75 / 102
NHLA grade
FAS
Table 7. — Part dimension limits used for the pricing structure of the cutting bill
Dimension
Length
Width
Attribute
Dimension stock
Panels
Flooring components
Minimum
(mm)
Maximum
(mm)
Minimum
(mm)
Maximum
(mm)
Minimum
(mm)
Maximum
(mm)
Short
290
650
350
650
x
x
Medium
750
1250
850
1250
350
1850
Long
1500
1800
1500
1800
x
x
Narrow
45
55
x
x
65
65
Medium
65
76
38
76
90
90
Wide
85
125
x
x
115
115
Table 8. — Hard maple pricing, and width/length factors
[Unselected, Green, net tally] (Hardwood Review Weekly March 2002)
NHLA grade
Price $
Length
factor
Width
factor
2 Common
590
1.0
0.95
1 Common
1020
1.73
1.0
FAS
1560
2.64
1.05
© Martin Caron
43
Table 9. — Product / grades pricing and price factor
Real Product
Simulator grade
$ / 1000 B.F
Price factor
Kitchen Cabinet 2F
Pan 2F
3000
1.79
Dimension stock 2F
2F
2700
1.59
Kitchen cabinet 1F
Pan 1F
2500
1.47
Dimension stock 1F
1F
1700
1.00
Strip flooring - Sel/Btr
Sup
1420
0.84
Strip flooring - 1 Common
Med
1135
0.67
Strip flooring - 2 Common
Inf
935
0.55
© Martin Caron
44
Table 10. —Optimisation parameters
Parameter
Value
Product
priority
Dimension stocks, pannels, flooring
Optimisation
process
Rip first, crosscut first, best of rip first /
crosscut first
Maximum
optimisation
time
60s per board (per process)
Rip saw kerf
4mm
Crosscut saw
kerf
4mm
Saw blades
All blades moveable
Prioritisation
technique
Yield, Value
© Martin Caron
45
238.6
238.6
Surface
Value
188.9
188.9
Surface
Value
154.2
154.2
Surface
Value
61.8%
(0.7)
65.6%
(0.7)
59.5%
(0.9)
55.6%
(0.8)
58.6%
(0.8)
abc
abc
64.4%
(1.0)
61.7%
(0.8)
67.5%
(1.0)
Yield
Yield
Paired
(Cross) (Rip+Cross) tests¹
Yield
(Rip)
0.000
abc
aab
70.6%
(0.6)
64.7%
(0.8)
70.7%
(1.1)
62.3%
(1.2)
74.0%
(0.6)
63.8%
(0.9)
Yield
Yield
Paired
abc
Yield
(Rip)
0.000
66.2%
(0.7)
abc
69.2%
(1.0)
73.4%
(0.5)
75.2%
(0.5)
Yield
Yield
Paired
Yield
(Rip)
1.60
(0.05)
2.13
(0.05)
VPBF³
(Rip)
1.63
(0.04)
© Martin Caron
³ VPBF and RPBF are expressed in US $ per board feet
² results of the two-way ANOVA classification
2.24
(0.05)
0.000
1.95
(0.05)
abc
1.73
(0.05)
Paired RPBF³
VPBF³
VPBF³
(Rip)
1.24
1.63
1.67
aab
(0.04)
(0.04)
(0.05)
0.000
aba
1.55
(0.05)
RPBF³
(Cross)
1.23
(0.05)
1.29
(0.06)
1.79
(0.09)
aba
1.34
(0.07)
2.24
(0.11)
1.96
(0.08)
2.90
(0.12)
1.93
(0.08)
2.83
(0.12)
1.88
(0.06)
2.38
(0.10)
RPBF³
(Cross)
Paired RPBF³
VPBF³
VPBF³
(Rip)
VPBF³
(Rip)
0.000
aba
2.18
(0.06)
3.24
(0.06)
3.20
(0.06)
2.69
(0.05)
RPBF³
(Cross)
0.95
(0.04)
Paired RPBF³
VPBF³
VPBF³
(Rip)
1.97
2.12
1.05
abc
(0.04)
(0.04)
(0.04)
VPBF³
(Rip)
2.07
(0.04)
0.000
p-values²
¹ results of the paired test comparison between the processes for each output variable
B.F
Priorization
Hard maple – No.3 common
p-values²
B.F
Priorization
p-values²
B.F
Priorization
Table 11. — Optimization results for the hard maple database
aba
aba
aba
0.000
1.84
(0.05)
abc
Paired
RPBF³
(Rip+Cross) tests¹
1.28
aab
(0.04)
0.000
1.37
(0.08)
2.31
(0.11)
Paired
RPBF³
(Rip+Cross) tests¹
0.000
2.22
(0.06)
Paired
RPBF³
(Rip+Cross) tests¹
1.10
abc
(0.06)
46
180.6
180.6
Surface
Value
215.9
215.9
Surface
Value
180.4
180.4
Surface
Value
62.7%
(1.0)
63.5%
(1.0)
61.4%
(1.2)
53.7%
(1.4)
58.9%
(1.15)
abc
abc
67.5%
(1.6)
61.9%
(1.2)
70.9%
(1.3)
Yield
Yield
Paired
Yield
(Rip)
0.000
abb
abc
66.7%
(0.8)
60.6%
(0.9)
67.7%
(0.9)
58.4%
(1.3)
72.2%
(0.7)
60.7%
(1.4)
Yield
Yield
Paired
aba
Yield
(Rip)
0.000
64.3%
(1.1)
abc
72.8%
(1.1)
74.5%
(0.7)
77.3%
(0.8)
Yield
Yield
Paired
Yield
(Rip)
1.37
(0.08)
1.61
(0.05)
VPBF³
(Rip)
1.25
(0.04)
© Martin Caron
³ VPBF and RPBF are expressed in US $ per board feet
² results of the two-way ANOVA classification
1.74
(0.05)
0.000
1.59
(0.05)
aab
1.22
(0.05)
Paired RPBF³
VPBF³
VPBF³
(Rip)
0.86
1.32
1.33
abb
(0.04)
(0.04)
(0.03)
0.000
abc
1.20
(0.05)
RPBF³
(Cross)
0.93
(0.04)
0.90
(0.06)
1.25
(0.06)
aaa
0.90
(0.06)
1.49
(0.08)
1.52
(0.07)
2.14
(0.07)
1.49
(0.06)
2.08
(0.07)
1.49
(0.06)
1.84
(0.06)
RPBF³
(Cross)
Paired RPBF³
VPBF³
VPBF³
(Rip)
VPBF³
(Rip)
0.000
aba
1.98
(0.10)
3.03
(0.10)
3.00
(0.10)
2.39
(0.08)
RPBF³
(Cross)
0.68
(0.05)
Paired RPBF³
VPBF³
VPBF³
(Rip)
1.70
1.88
0.92
abc
(0.05)
(0.07)
(0.06)
VPBF³
(Rip)
1.94
(0.06)
0.000
p-values²
¹ results of the paired test comparison between the processes for each output variable
B.F
Priorization
Black cherry –No.3 common
p-values²
B.F
Priorization
p-values²
B.F
Priorization
Table 12. — Optimization results for the black cherry database
aba
abc
aaa
0.000
1.35
(0.05)
aab
Paired
RPBF³
(Rip+Cross) tests¹
0.94
abb
(0.03)
0.000
0.93
(0.07)
1.55
(0.07)
Paired
RPBF³
(Rip+Cross) tests¹
0.000
2.01
(0.10)
Paired
RPBF³
(Rip+Cross) tests¹
0.86
abc
(0.07)
47
Figure 1. —BorealScan image acquisition system
© Martin Caron
48
Figure 2. ― Products distribution for hard maple
Products distribution for hard maple
100%
Percentage of production (%)
90%
80%
70%
60%
Flooring
50%
Pannels
40%
Dimension stock
30%
20%
10%
0%
1 Common surface
1 Common value
2 Common surface
2 Common value
3 Common surface
3 Common value
NHLA grade - prioritization
© Martin Caron
49
Figure 3. ― Products distribution for black cherry
Products distribution for black cherry
100%
Percentage of production (%)
90%
80%
70%
60%
Flooring
50%
Pannels
40%
Dimension stock
30%
20%
10%
0%
1 Common surface
1 Common value
2 Common - 2 Common surface
value
3 Common surface
3 Common value
NHLA grade - prioritization
© Martin Caron
50
3. Conclusion
Cette étude a été accomplie dans le but de comparer les effets de prioriser les opérations
d’une usine de débitage de composants de bois francs selon la valeur produite des pièces, en
utilisant une structure de prix inspirée des règles de classification des bois francs de la
NHLA, en comparaison avec l’utilisation d’une stratégie de placement basée seulement sur le
rendement matière.
La comparaison nécessitait l’examen des effets et des interactions entre trois procédés de
débitage (délignage en tête, tronçonnage en tête et meilleur entre délignage en tête et
tronçonnage en tête) et deux mécanismes de priorisation (la maximisation de la valeur
produite et la maximisation du rendement matière) utilisés dans « l’Optimiseur deux axes »
développé par le CRIQ.
Les résultats globaux ont confirmé l’hypothèse de la supériorité d’une stratégie de
priorisation des pièces selon la maximisation de la valeur produite. Les simulations utilisant
un mécanisme de priorisation selon la valeur des pièces ont obtenu un rendement inférieur
aux simulations maximisant le rendement matière, mais dans tous les cas ont produit un
RPBF nettement supérieur. Ce résultat est majeur car il met en lumière un fait qui n’a pas été
profondément analysé dans les ouvrages précédents ; « Faire plus, ne veut pas dire,
nécessairement faire mieux ». Un rendement matière supérieur n’apporte pas directement un
retour monétaire plus élevé. Ceci démontre que les indicateurs courants de performance pour
les usines de débitage de bois franc (i.e. le rendement matière) sont basés sur un résultat qui
n’apporte pas nécessairement une valeur optimale de pièces, donc qui n’apporte pas à l’usine
et l’entreprise la meilleure valeur possible en retour sur le bois qu’elle utilise.
Les rendements obtenus dans une optimisation maximisant le surface produite ont été plus
élevés pour les deux essences de bois en utilisant le délignage en tête pour les grades
supérieurs utilisés (No.1 Commun). La différence entre les rendements entre les deux
procédés « non-sélectifs » était beaucoup plus faible dans le grade No.2 Commun (voir
© Martin Caron
51
même nulle dans le cas de l’érable). Le tronçonnage en tête a apporté de meilleurs
rendements pour les deux essences de bois quand le grade inférieur était utilisé (No.3
Commun). Ces résultats confirment les relations suggérées par Wiedenbeck (2001) dans sa
comparaison entre les procédés de délignage en tête et de tronçonnage en tête.
Les résultats pointent aussi vers le haut potentiel apporté par un procédé « sélectif » comme
le procédé de sélection entre la meilleure solution du délignage en tête et du tronçonnage en
tête avec un mécanisme de priorisation basé sur la maximisation de la surface. Les
rendements obtenus sont supérieurs et conservés à un niveau plus élevés malgré l’utilisation
de grades NHLA inférieurs. Le procédé « sélectif » s’adapte aux dimensions et
caractéristiques des planches. Ceci démontre qu’un procédé « sélectif » utilisé dans une
usine de débitage apporterait un rendement matière plus élevé, plus constant et plus robuste
aux variations de mélange de bois utilisé lors du débitage, quand le rendement est utilisé
comme indicateur de performance.
Les résultats démontrent aussi clairement un RPBF nettement supérieur produit pour les
grades supérieur et intermédiaires utilisés (No.1 Commun et No.2 Commun) en délignage en
tête avec une maximisation de la valeur produite des pièces. Il y a deux sources possibles
pour cette hausse significative de valeur produite : 1) une longueur moyenne plus élevée pour
les pièces produites, 2) un grade moyen plus élevé pour les pièces produites. Les résultats
observés n’ont pas démontré d’augmentation du grade des pièces produites en délignage en
tête versus en tronçonnage en tête. Les résultats indiquent donc qu’en priorisant le débitage
selon la maximisation de la valeur des pièces produites, le délignage en tête produit des
pièces plus longues que le tronçonnage en tête.
Les résultats ont confirmé la force du RPBF pour comparer différents procédés de débitage
utilisant différents mélanges de produits et différents grades de bois en entrée. Ils
démontrent que le rendement est une indicateur complexe et potentiellement incomplet
quand utilisé pour comparer des usines différentes. Par sa définition, le rendement matière
ne contient pas d’information sur les pièces produites, le volume de bois nécessaire pour
produire ces pièces, ni le coût du bois nécessaire pour la production.
© Martin Caron
52
Il est important de noter que cette étude a été faite sur la base d’une demande infinie de
pièces à produire en relation avec le contexte de producteur de composants. Les résultats de
cette étude tendent à supposer que l’utilisation d’une structure de prix pour les pièces dans un
simulateur utilisant une stratégie de priorisation de valeur produite va apporter de meilleurs
résultats qu’une stratégie d’optimisation selon le rendement matière.
Des recherches futures comparant les effets de la stratégie de priorisation entre la valeur
produite et la surface produite devrait être faites en utilisant une demande finie pour les
pièces du carnet de commande. Ceci correspondant au contexte de l’industrie du meuble par
exemple.
© Martin Caron
53
4. Liste des références
Bouffard J.-F., Beauregard R. et T. Lihra. 2002. Hardwood rough mill optimization :
comparison of various approaches and simulation software. Minutes du 4ième Groupe de
travail de l’IUFRO WP S5-01-04 « Connection between Forest Resources and Wood
Quality : Modelling Approaches and Simulation Software », Harrison (Vancouver) BC, 9-13
septembre 2002.
Bowker, A. H. et G.J. Lieberman.1972. Engineering statistics 2nd Edition. Prentice-Hall
press. 1972. p264.
Buehlmann, U. P. 1998. Understanding the relationship of lumber yield and cutting bill
requirements : a statistical approach. Thèse de doctorat, non publiée, Virginia Polytechnic
Institute and State University, Blacksburg, VO, 209p.
Conners, R. W., D. E. Kline, P. A. Araman, an T. H. Drayer 1997. Machine vision
technology for the forest product industry. IEEE computer innovative technology for
computer professionals 30(7):43-48.
Caron, M. 2001. Optimiseur 2 axes : Manuel de l’utilisateur [CRIQ rough mill simulator
user’s guide version française seulement]. Centre de Recherche Industrielle du Québec.
Sainte-Foy, Québec, 10-12-2001. 55p.
El-Radi, T. E., S. H. Bullard and P. H. Steele. 1994. A performance evaluation of edging and
trimming operations in U.S. hardwood sawmills. Canadian Journal of Forestry Research
23:1450-1456.
Gatchell, C. J. 1990 The effect of crook on yields when processing narrow lumber with a
fixed arbor gang ripsaw. Forest Products Journal 40(5):9-17.
© Martin Caron
54
Gatchell, C. J. and R. E. Thomas. 1997. Within-grade quality differences for 1 and 2A
Common lumber affect processing and yields when gang-ripping red oak lumber. Forest
Gatchell, C.J and R. E. Thomas. 1998. Data bank for kiln-dried red oak lumber. Research
paper NE-245 USDA Forest service, Northeastern Forest Experiment Station, Radnor, PA.
60p.
Hall, S. P. , R. A. Wysk, E. M. Wengert, and M. H. Agee. 1980. Yield distribution and cost
comparison of a cross-cut first and a gang-rip-first rough mill producing hardwood
dimension stair parts. Forest Products Journal 30(5):34-39.
Hamner, P. C., Bond, B. Wiedenbeck, J. 2002. The effect of lumber length on part yields
in gang-rip-first rough mills. Forest Products Journal 52(5):71-76.
Hardwood review weekly. 2002. Hardwood pricing, hard maple green unselected, northern
pricing. March 2002. Vol 18. Issue 28.
Hoff, K., N. Fisher, S. Miller, and A. Webb. 1997. Sources of competitiveness for secondary
wood products firms : A review of literature and research issues. Forest Products Journal
47(2):31-37.
Industrie Canada. 2002. La série des cadres de compétivité sectorielle : Les produits
forestiers. Site web Strategis : http://strategis.ic.gc.ca/SSGF/fb01003f.html
Labbé S. 2002. CEO of the Québec Wood Export Bureau. Communication personnelle.
Mitchell, P. 2002. Setting priorities on rough mill cutoff saws. Wood Digest May 2002 :
41-45.
Mullin, S. 1990. Why switch to rip-first ? Furniture design & manufacturing, 62(9):36-42.
© Martin Caron
55
National Hardwood Lumber Association. 1998. NHLA 1998 Hardwood Grading rules and
inspection handbook. Memphis, TN, USA. 90p.
Steele, H. P. And Wiedenbeck, J. K. 1999. The influence of lumber grade on machine
productivity in the rough mill. Forest products Journal 49(9) : 48-54.
Thomas, R. E. 1996a. ROMI-RIP : An analysis tool for rip-first rough mill operations.
Thomas, R. E. 1996b. Prioritizing parts from cutting bills when gang ripping first. Forest
Thomas, R. E. 1997. ROMI-CROSS : An analysis tool for crosscut-first rough mill
operations. Forest Products Journal. 48(2):68-72.
Thomas, R. E. And Buehlmann. U. 2002.Validation of the ROMI-RIP rough mill simulator.
Forest Products Journal 52(2) : 23-29.
Wiedenbeck, J. K. 2001. Deciding between crosscut and Rip-first processing. Wood and
Wood Products August 2001. 5p.
Wiedenbeck, J. K. And C. Scheerer. 1996. A report on rough mill yield practices and
performances – how well are we doing ? Twenty-fourth annual Hardwood Symposium of the
Hardwood Research Council, p121-128.
© Martin Caron
56
Annexe 1 : Prix calculés
Longueur Largeur Surface
(mm)
(mm)
(mm²)
Grade
Facteur Facteur
Longueur Largeur
Facteur
Grade
prix/mm²
Prix calculé
290.00
65.00
18850
2F
1
1
1.59
1.8298E-05
0.54842
290.00
76.00
22040
2F
1
1
1.59
1.8298E-05
0.64123
290.00
85.00
24650
2F
1
1
1.59
1.8298E-05
0.71716
350.00
65.00
22750
2F
1
1
1.59
1.8298E-05
0.66188
350.00
70.00
24500
2F
1
1
1.59
1.8298E-05
0.71280
350.00
76.00
26600
2F
1
1
1.59
1.8298E-05
0.77390
400.00
65.00
26000
2F
1
1
1.59
1.8298E-05
0.75644
450.00
45.00
20250
2F
1
0.95
1.59
1.8298E-05
0.55969
450.00
70.00
31500
2F
1
1
1.59
1.8298E-05
0.91646
450.00
76.00
34200
2F
1
1
1.59
1.8298E-05
0.99501
450.00
85.00
38250
2F
1
1.05
1.59
1.8298E-05
1.16848
450.00 125.00
56250
2F
1
1.05
1.59
1.8298E-05
1.71835
500.00
65.00
32500
2F
1
1
1.59
1.8298E-05
0.94555
500.00
70.00
35000
2F
1
1
1.59
1.8298E-05
1.01828
500.00
76.00
38000
2F
1
1
1.59
1.8298E-05
1.10557
500.00
85.00
42500
2F
1
1.05
1.59
1.8298E-05
1.29831
550.00 105.00
57750
2F
1
1.05
1.59
1.8298E-05
1.76418
600.00
65.00
39000
2F
1
1
1.59
1.8298E-05
1.13466
600.00
85.00
51000
2F
1
1
1.59
1.8298E-05
1.48378
650.00
55.00
35750
2F
1
0.95
1.59
1.8298E-05
0.98810
650.00
65.00
42250
2F
1
1
1.59
1.8298E-05
1.22921
650.00
85.00
55250
2F
1
1.05
1.59
1.8298E-05
1.68781
650.00 105.00
68250
2F
1
1.05
1.59
1.8298E-05
2.08494
750.00
65.00
48750
2F
1.73
1
1.59
1.8298E-05
2.45370
750.00
85.00
63750
2F
1.73
1.05
1.59
1.8298E-05
3.36912
750.00 125.00
93750
2F
1.73
1.05
1.59
1.8298E-05
4.95459
850.00
65.00
55250
2F
1.73
1
1.59
1.8298E-05
2.78086
1050.00
45.00
47250
2F
1.73
0.95
1.59
1.8298E-05
2.25929
1050.00
65.00
68250
2F
1.73
1
1.59
1.8298E-05
3.43518
1050.00
85.00
89250
2F
1.73
1.05
1.59
1.8298E-05
4.71677
1050.00 105.00
110250
2F
1.73
1.05
1.59
1.8298E-05
5.82659
2F
1.73
1
1.59
1.8298E-05
4.08950
1250.00
65.00
81250
1250.00
85.00
106250
2F
1.73
1.05
1.59
1.8298E-05
5.61520
1500.00
65.00
97500
2F
2.64
1
1.59
1.8298E-05
7.48875
1500.00
75.00
112500
2F
2.64
1
1.59
1.8298E-05
8.64086
© Martin Caron
57
1500.00
85.00
127500
2F
2.64
1.05
1.59
1.8298E-05
10.28263
1600.00
65.00
104000
2F
2.64
1
1.59
1.8298E-05
7.98800
1600.00
75.00
120000
2F
2.64
1
1.59
1.8298E-05
9.21692
1600.00
85.00
136000
2F
2.64
1.05
1.59
1.8298E-05
10.96814
1600.00 105.00
168000
2F
2.64
1.05
1.59
1.8298E-05
13.54888
1800.00
55.00
99000
2F
2.64
0.95
1.59
1.8298E-05
7.22376
1800.00
65.00
117000
2F
2.64
1
1.59
1.8298E-05
8.98650
1800.00
75.00
135000
2F
2.64
1
1.59
1.8298E-05
10.36904
1800.00
85.00
153000
2F
2.64
1.05
1.59
1.8298E-05
12.33915
290.00
65.00
18850
1F
1
1
1
1.8298E-05
0.34492
290.00
76.00
22040
1F
1
1
1
1.8298E-05
0.40329
290.00
85.00
24650
1F
1
1.05
1
1.8298E-05
0.47360
350.00
65.00
22750
1F
1
1
1
1.8298E-05
0.41628
350.00
70.00
24500
1F
1
1
1
1.8298E-05
0.44830
350.00
76.00
26600
1F
1
1
1
1.8298E-05
0.48673
400.00
65.00
26000
1F
1
1
1
1.8298E-05
0.47575
450.00
45.00
20250
1F
1
0.95
1
1.8298E-05
0.35201
450.00
70.00
31500
1F
1
1
1
1.8298E-05
0.57639
450.00
76.00
34200
1F
1
1
1
1.8298E-05
0.62579
450.00
85.00
38250
1F
1
1.05
1
1.8298E-05
0.73489
450.00 125.00
56250
1F
1
1.05
1
1.8298E-05
1.08073
500.00
65.00
32500
1F
1
1
1
1.8298E-05
0.59469
500.00
70.00
35000
1F
1
1
1
1.8298E-05
0.64043
500.00
76.00
38000
1F
1
1
1
1.8298E-05
0.69532
500.00
85.00
42500
1F
1
1.05
1
1.8298E-05
0.81655
550.00 105.00
57750
1F
1
1.05
1
1.8298E-05
1.10954
600.00
65.00
39000
1F
1
1
1
1.8298E-05
0.71362
600.00
85.00
51000
1F
1
1.05
1
1.8298E-05
0.97986
650.00
55.00
35750
1F
1
0.95
1
1.8298E-05
0.62145
650.00
65.00
42250
1F
1
1
1
1.8298E-05
0.77309
650.00
85.00
55250
1F
1
1.05
1
1.8298E-05
1.06151
650.00 105.00
68250
1F
1
1.05
1
1.8298E-05
1.31128
750.00
65.00
48750
1F
1.73
1
1
1.8298E-05
1.54321
750.00
85.00
63750
1F
1.73
1.05
1
1.8298E-05
2.11894
750.00 125.00
93750
1F
1.73
1.05
1
1.8298E-05
3.11609
850.00
65.00
55250
1F
1.73
1
1
1.8298E-05
1.74897
1050.00
45.00
47250
1F
1.73
0.95
1
1.8298E-05
1.42094
1050.00
65.00
68250
1F
1.73
1
1
1.8298E-05
2.16049
1050.00
85.00
89250
1F
1.73
1.05
1
1.8298E-05
2.96652
1050.00 105.00
110250
1F
1.73
1.05
1
1.8298E-05
3.66452
1250.00
65.00
81250
1F
1.73
1
1
1.8298E-05
2.57201
1250.00
85.00
106250
1F
1.73
1.05
1
1.8298E-05
3.53157
1500.00
65.00
97500
1F
2.64
1
1
1.8298E-05
4.70991
1500.00
75.00
112500
1F
2.64
1
1
1.8298E-05
5.43451
© Martin Caron
58
1500.00
85.00
127500
1F
2.64
1.05
1
1.8298E-05
6.46706
1600.00
65.00
104000
1F
2.64
1
1
1.8298E-05
5.02390
1600.00
75.00
120000
1F
2.64
1
1
1.8298E-05
5.79681
1600.00
85.00
136000
1F
2.64
1.05
1
1.8298E-05
6.89820
1600.00 105.00
168000
1F
2.64
1.05
1
1.8298E-05
8.52131
1800.00
55.00
99000
1F
2.64
0.95
1
1.8298E-05
4.54325
1800.00
65.00
117000
1F
2.64
1
1
1.8298E-05
5.65189
1800.00
75.00
135000
1F
2.64
1
1
1.8298E-05
6.52141
1800.00
85.00
153000
1F
2.64
1.05
1
1.8298E-05
7.76047
350.00
1.00
350 Pan 2F
1
1
1.76
1.8298E-05
0.01127
400.00
1.00
400 Pan 2F
1
1
1.76
1.8298E-05
0.01288
450.00
1.00
450 Pan 2F
1
1
1.76
1.8298E-05
0.01449
500.00
1.00
500 Pan 2F
1
1
1.76
1.8298E-05
0.01610
550.00
1.00
550 Pan 2F
1
1
1.76
1.8298E-05
0.01771
650.00
1.00
650 Pan 2F
1
1
1.76
1.8298E-05
0.02093
850.00
1.00
850 Pan 2F
1.73
1
1.76
1.8298E-05
0.04736
1050.00
1.00
1050 Pan 2F
1.73
1
1.76
1.8298E-05
0.05850
1250.00
1.00
1250 Pan 2F
1.73
1
1.76
1.8298E-05
0.06964
1500.00
1.00
1500 Pan 2F
2.64
1
1.76
1.8298E-05
0.12753
1800.00
1.00
1800 Pan 2F
2.64
1
1.76
1.8298E-05
0.15304
350.00
1.00
350 Pan 1F
1
1
1.47
1.8298E-05
0.00941
400.00
1.00
400 Pan 1F
1
1
1.47
1.8298E-05
0.01076
450.00
1.00
450 Pan 1F
1
1
1.47
1.8298E-05
0.01210
500.00
1.00
500 Pan 1F
1
1
1.47
1.8298E-05
0.01345
550.00
1.00
550 Pan 1F
1
1
1.47
1.8298E-05
0.01479
650.00
1.00
650 Pan 1F
1
1
1.47
1.8298E-05
0.01748
850.00
1.00
850 Pan 1F
1.73
1
1.47
1.8298E-05
0.03955
1050.00
1.00
1050 Pan 1F
1.73
1
1.47
1.8298E-05
0.04886
1250.00
1.00
1250 Pan 1F
1.73
1
1.47
1.8298E-05
0.05817
1500.00
1.00
1500 Pan 1F
2.64
1
1.47
1.8298E-05
0.10652
1800.00
1.00
1800 Pan 1F
2.64
1
1.47
1.8298E-05
0.12782
1.00
65.00
1
0.95
0.84
1.8298E-05
0.00095
1.00
90.00
90 Sup
1
1
0.84
1.8298E-05
0.00138
1.00 115.00
115 Sup
1
1.05
0.84
1.8298E-05
0.00186
65 Med
1
0.95
0.67
1.8298E-05
0.00076
65 Sup
1.00
65.00
1.00
90.00
90 Med
1
1
0.67
1.8298E-05
0.00110
1.00 115.00
115 Med
1
1.05
0.67
1.8298E-05
0.00148
0.95
0.55
1.8298E-05
0.00062
1.00
65.00
65 Inf
1
1.00
90.00
90 Inf
1
1
0.55
1.8298E-05
0.00091
1.00 115.00
115 Inf
1
1.05
0.55
1.8298E-05
0.00122
© Martin Caron
59

Martin Caron Comparaison de l`optimisation selon le prix et selon le

Transcription

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